<p>Click the image below to download our infographic on data literacy for responsible AI. For a deeper dive on how to spread the responsible use of AI with data literacy, explore our <a target="_blank" href="https://www.datacamp.com/resources/whitepapers/data-literacy-for-responsible-ai">exclusive guide</a>.</p>
<p><center><a target="_blank" href="https://s3.amazonaws.com/assets.datacamp.com/email/other/Data+Literacy+for+Responsible+AI+Infographic.pdf"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624199048/Data_Literacy_for_Responsible_AI_Infographic_wibbvb.pdf" /></center></a></p>
<p><br><p><center><a target="_blank" href="https://www.datacamp.com/business/demo"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603718763/banner_style_2_m573ef.png" /></a></p></p>


https://www.datacamp.com/community/blog/data-literacy-for-responsible-ai-infographic

Our infographic explains the importance of developing responsible AI and what it means for organizations today.

Data Literacy for Responsible AI [Infographic]

<p>DataCamp Digest is our newsletter aimed at providing the most up-to-date insights and news on all things data science. In this edition of the newsletter, we explore the future of jobs, discuss new tools and technologies, explore the future of AI regulation, and more.</p>
<p><center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1612888906/insights_qhc4kg.png" /></a></center></p> 

<h4 id="-the-future-of-jobs-in-the-era-of-ai-https-www-bcg-com-publications-2021-impact-of-new-technologies-on-jobs-boston-consulting-group"><a target="_blank" href="https://www.bcg.com/publications/2021/impact-of-new-technologies-on-jobs">The future of jobs in the era of AI</a> | Boston Consulting Group</h4>
<p>This report by BCG studies the impact of AI and automation on the job market in several key countries. It prescribes actions governments and organizations can take to alleviate automation woes, key amongst them being talent development.</p>
<h4 id="-new-ai-regulations-are-coming-are-you-ready-https-hbr-org-2021-04-new-ai-regulations-are-coming-is-your-organization-ready-harvard-business-review"><a target="_blank" href="https://hbr.org/2021/04/new-ai-regulations-are-coming-is-your-organization-ready">New AI regulations are coming, are you ready?</a> | Harvard Business Review</h4>
<p>The use of AI systems is becoming mainstream and regulators are setting guardrails to ensure the responsible use of AI. In this article, Andrew Burt outlines recent AI regulation efforts, and how organizations can prepare and ensure their models are compliant.</p>
<h4 id="-analytics-is-a-mess-https-benn-substack-com-p-analytics-is-a-mess-benn-stancil"><a target="_blank" href="https://benn.substack.com/p/analytics-is-a-mess">Analytics is a mess</a> | Benn Stancil</h4>
<p>Benn Stancil from Mode Analytics discusses the creative, messy, and non-linear nature of data science and analytics. If you ever feel stuck when solving a data science problem, remember that “A mess is the process of progress”.</p>
 <p><br><center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1612888907/Editor_s_Picks_nj17hh.png" /></a></center></p>

<h4 id="-openai-launches-100m-startup-fund-https-openai-com-fund-openai"><a target="_blank" href="https://openai.com/fund/">OpenAI launches $100M startup fund</a> | OpenAI</h4>
<p>OpenAI, the organization behind GPT-3, is launching a $100M startup fund for startups that leverage OpenAI tools specifically in “in fields where artificial intelligence can have a transformative effect—like health care, climate change, and education—and where AI tools can empower people by helping them be more productive.”</p>
<h4 id="-good-data-scientist-bad-data-scientist-https-ianwhitestone-work-good-ds-bad-ds-ian-whitestone"><a target="_blank" href="https://ianwhitestone.work/good-ds-bad-ds/">Good data scientist bad data scientist</a> | Ian Whitestone</h4>
<p>A short and snappy post that outlines what differentiates a good data scientist from a bad one.</p>
<h4 id="-speech-recognition-with-no-supervision-https-ai-facebook-com-blog-wav2vec-unsupervised-speech-recognition-without-supervision-facebook-ai"><a target="_blank" href="https://ai.facebook.com/blog/wav2vec-unsupervised-speech-recognition-without-supervision">Speech recognition with no supervision</a> | Facebook AI</h4>
<p>The team at Facebook AI leverages self-supervised learning to develop a speech recognition model that can learn to identify any language without any training data. This is huge for the recognition of languages where there is limited training data.</p>
<p><br><center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1612889346/Inspiration2_wxr25i.png" /></a></center></p>

<h4 id="-powerbi-coming-to-a-notebook-near-you-https-powerbi-microsoft-com-en-us-blog-announcing-power-bi-in-jupyter-notebooks-microsoft"><a target="_blank" href="https://powerbi.microsoft.com/en-us/blog/announcing-power-bi-in-jupyter-notebooks/">PowerBI coming to a notebook near you</a> | Microsoft</h4>
<p>Microsoft created a new Python package that lets you embed PowerBII directly in Jupyter Notebooks. This will make it much easier to embed compelling, editable data stories directly in Jupyter Notebooks.</p>
<h4 id="-lessons-on-ml-platforms-from-netflix-doordash-spotify-and-more-https-towardsdatascience-com-lessons-on-ml-platforms-from-netflix-doordash-spotify-and-more-f455400115c7-ernest-chan"><a target="_blank" href="https://towardsdatascience.com/lessons-on-ml-platforms-from-netflix-doordash-spotify-and-more-f455400115c7">Lessons on ML Platforms — from Netflix, DoorDash, Spotify, and more</a> | Ernest Chan</h4>
<p>Ernest Chan breaks down the components of the ML platforms that power the models of the most sophisticated data companies today.</p>
<h4 id="-microsoft-recommenders-https-github-com-microsoft-recommenders-microsoft"><a target="_blank" href="https://github.com/microsoft/recommenders">Microsoft Recommenders</a> | Microsoft</h4>
<p>A GitHub repository created by Microsoft that covers their best practices in creating recommender systems, with Jupyter Notebooks included. Check out the related projects section for similar repositories on NLP, computer vision, and more.</p>
<p><br><center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1612888937/Tools_and_Techniques_sf5hut.png" /></a></center></p>

<h4 id="-thinking-in-data-https-marketplace-visualstudio-com-items-itemname-dynamicwebpaige-thinking-in-data-paige-bailey"><a target="_blank" href="https://marketplace.visualstudio.com/items?itemName=dynamicwebpaige.thinking-in-data">Thinking in data</a>  | Paige Bailey</h4>
<p>If you use visual studio this one’s for you. Thinking in data is a set of VS Code extensions that supercharge your analytics and data science workflows on visual studio.</p>
<h4 id="-greykite-a-flexible-intuitive-and-fast-forecasting-library-https-engineering-linkedin-com-blog-2021-greykite-a-flexible-intuitive-and-fast-forecasting-library-reza-hosseini"><a target="_blank" href="https://engineering.linkedin.com/blog/2021/greykite--a-flexible--intuitive--and-fast-forecasting-library">Greykite: A flexible, intuitive, and fast forecasting library</a> | Reza Hosseini</h4>
<p>The data team at LinkedIn has open-sourced its Greykite forecasting library on Python. Check out the blog post to find out how it differs from other forecasting packages, and how the team applied it to forecasting problems at LinkedIn.   </p>
<h4 id="-flat-data-https-octo-github-com-projects-flat-data-github"><a target="_blank" href="https://octo.github.com/projects/flat-data">Flat data</a> | GitHub</h4>
<p>The team at GitHub released some tools that make it easy to work with and view data on GitHub.</p>
 <p><br><center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1612889508/From_DataCamp_c6kasv.png" /></a></center></p>

<h4 id="-webinar-building-data-cultures-https-www-datacamp-com-resources-webinars-building-data-cultures-"><a target="_blank" href="https://www.datacamp.com/resources/webinars/building-data-cultures">Webinar: Building Data Cultures</a></h4>
<p>In this webinar, regional Chief Data and Analytics Officer at Allianz Benelux Sudaman Thoppan Mohanchandralal deep dives into the ins and outs of building data cultures.</p>
<h4 id="-white-paper-your-organization-s-guide-to-data-maturity-https-www-datacamp-com-resources-whitepapers-your-organizations-guide-to-data-maturity-"><a target="_blank" href="https://www.datacamp.com/resources/whitepapers/your-organizations-guide-to-data-maturity">White Paper: Your Organization&#39;s Guide to Data Maturity</a></h4>
<p>In this guide, we outline a framework for evaluating, and scaling data maturity throughout the organization, define the various data maturity stages an organization goes through, and draw a path of initiatives organizations can take to achieve data fluency.</p>
<h4 id="-podcast-63-the-past-and-present-of-data-science-https-www-datacamp-com-community-podcast-the-past-and-present-of-data-science-"><a target="_blank" href="https://www.datacamp.com/community/podcast/the-past-and-present-of-data-science">Podcast: #63 The Past and Present of Data Science</a></h4>
<p>In this episode of DataFramed, Adel speaks with Sergey Fogelson, Vice President of Data Science and Modeling at Viacom on how data science has evolved over the past decade, and the remaining large-scale challenges facing data teams today.</p>
<p><br><center><a target="_blank" href="https://www.datacamp.com/business/demo"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603718763/banner_style_2_m573ef.png" /></a></center></p>


https://www.datacamp.com/community/blog/digest-june-2021

Read our favorite articles from the last month.

DataCamp Digest June 2021: PowerBI in Jupyter Notebooks, the Future of Jobs, and More

<p>In college, I am planning to study computer science and economics. However, I did not want to wait to learn the basics of data science until I got to college. In that sense, DataCamp has truly helped me overcome the barriers of time and space.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1623766953/Screen_Shot_2021-06-15_at_10.21.57_AM_gqricw.png" /></center><br>

<p>I got access to <a target="_blank" href="https://www.datacamp.com/">DataCamp</a> as a member of Code for Nepal, a non-profit organization working to increase digital literacy and data literacy in Nepal. <a target="_blank" href="http://datacamp.com/donates">DataCamp Donates</a> is their <a target="_blank" href="https://www.datacamp.com/community/blog/datacamp-donates-50-partners">global effort</a> to build data skills and has donated thousands of subscriptions to organizations and schools globally.</p>
<p>As I worked to enhance the learning experiences of hundreds of others who were selected as Code for Nepal’s <a target="_blank" href="https://codefornepal.org/2021/02/14/launching-a-fellowship-program-to-build-data-skills-of-over-500-nepalis-globally">Data Fellows</a>, I was increasing my own knowledge of data science and familiarity with DataCamp. The courses on DataCamp are developed to meet the expectations of learners at every level—I have found them very helpful.</p>
<p>I had no prior experience with programming languages. I knew a little bit of HTML and Q-basic that I had learned in middle school. As a total beginner at the start of my DataCamp journey, I discovered Introduction to Python, Introduction to R, and Introduction to SQL and they were the perfect place to start.</p>
<p>The next step in my journey was having to decide whether I wanted to start with an individual course or a track. The courses started with the basics: types, variables, basic calculations. Therefore, even as a beginner that has never written or read a single line of code, I was able to follow the material presented very easily.</p>
<p>The courses on DataCamp take place on your browser so you don’t have to download anything to learn. I am very impressed with the concept of interactive e-learning with DataCamp as it allowed me to become active with my own learning experience. Interestingly, DataCamp XP is an experience point system that rewards learners like me for engaging in learning. XP is awarded to learners for progressing in courses, exercises, and projects.</p>
<p>When I use hints or show answer methods for solving problems a certain amount of my XP was used which motivated me to try and solve problems on my own as I did not want to lose the hard-earned XP.</p>
<p>DataCamp also has a section dedicated to getting real-life experience with projects. On the live events tab, there are always upcoming events like webinars and live training sessions to learn about data-driven organizations. I am thrilled to learn new things every day. DataCamp feels like a roadmap where I am in a data car that leads me safely by minimizing risk and providing clarity to reach my destination as a data scientist. This journey has to be my favorite of all time.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1623766953/Screen_Shot_2021-06-15_at_10.22.16_AM_iyf21n.png" /></center><br>

<p>All our DataCamp Donates fellows have had a positive experience learning different technologies like python, R, SQL, and Machine learning. In the era of the COVID-19 pandemic, as a high school student, I realized it is necessary to reinforce the various learning opportunities that prioritize strengthening my data skills and DataCamp was the best platform for it.</p>
<p>Finally, let me share that when I finished my first course, I was very proud of myself. After all, I managed to accomplish a significant step towards eventually becoming a data expert. I realized that becoming data fluent would require more time and dedication. I look forward to completing more courses, tracks, and projects that DataCamp has to offer.</p><br><br>
<p><i>Anshu recently graduated high school in Kathmandu, Nepal, and is matriculating to the University of Mary Washington in the fall. Code for Nepal’s mission is to help create a digitally and data-empowered Nepal together where everyone can thrive. Learn more about them by visiting <a target="_blank" href="http://codefornepal.org">their website</a>, where this post was <a target="_blank" href="https://codefornepal.org/2021/04/11/datacamp-experience.html">originally featured</a>. </i></p>
<br><center><a target="_blank" href="http://datacamp.com/donates"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1623777889/DCD_logo_regular_s7iazm.png" /></a></center>

<br><center><a target="_blank" href="http://codefornepal.org"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1623446367/code_for_nepal_3_zsmms5.png" /></a></center><br>


https://www.datacamp.com/community/blog/donates-stories-anshu-adhikari

A young DataCamp Donates scholarship recipient from Nepal shares her experience spending the last year learning.

How DataCamp Is Helping Me Learn Data Science for Free

<p>Welcome to the golden age of data. Today, companies have the privilege of collecting and storing ever-increasing amounts of data in all shapes and sizes. That’s great news, but how good are companies at extracting value from data? Not excellent, according to a <a target="_blank" href="https://www.accenture.com/_acnmedia/pdf-108/accenture-closing-data-value-gap-fixed.pdf">Forrester report</a> that cites that 60%to 73% of all data within an enterprise remains unused for analytics in 2019.</p>
<p>Why is that so? One of the reasons companies are stopping short of becoming fully data-driven, according to <a target="_blank" href="https://www.accenture.com/_acnmedia/PDF-115/Accenture-Human-Impact-Data-Literacy-Latest.pdf">Accenture</a>, is the lack of data skills that stifles workplace productivity. Surprisingly, in a survey of 9,000 companies, Accenture found that three-quarters of the employees feel overwhelmed or unhappy when working with data. Some employees report feeling as such because they are not ‘keeping up’ with the rapid changes in technological transformations. In fact, 59% of employees report feeling burnt out when using business intelligence and data analytics tools. Such unhappiness can be averted if employees are equipped with the skills required to deal with data, or, more precisely, data literacy.</p>
<p><a target="_blank" href="https://www.datacamp.com/tracks/data-literacy-fundamentals">Data literacy</a> can be defined as the ability to read, write, analyze, and communicate with data. Much like how one does not need to be Shakespeare to be considered literate, one does not need to become a data scientist to be data literate.  Data literacy is not about learning every single programming language or the most advanced data science skills. Instead, it is about understanding and making decisions with data.</p>
<p>Achieving data literacy is an essential step in a company’s roadmap to become data fluent. More concretely, it helps employees make <a target="_blank" href="https://www.datacamp.com/courses/data-driven-decision-making">data-driven decisions</a>, interact with data critically, form effective <a target="_blank" href="https://www.datacamp.com/resources/webinars/how-data-governance-enables-scalable-data-science">data governance</a>, and make ethical data decisions.</p>
<h2 id="data-literacy-is-the-keystone-to-making-data-driven-decisions">Data literacy is the keystone to making data-driven decisions</h2>
<p><a target="_blank" href="https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/advanced-analytics-nine-insights-from-the-c-suite">McKinsey</a> found that employees are more likely to buy into a change when they understand it. Employees who understand both data science and its business application can spearhead the end-to-end development of use cases, and in turn influence others to make data-driven decisions.</p>
<p>For instance, the <a target="_blank" href="https://www.wsj.com/articles/the-houston-astros-a-case-study-of-the-transformative-power-of-analytics-1532102127">Houston Astros</a> used analytics to build its competitive advantage in a zero-sum industry, paving its way to victory in the Major League Baseline in 2017. Their secret? Executives who champion data literacy and make data-driven decisions. They employed data literate coaches (who can program in SQL) who could translate data insights to a language understood by players. Beyond that, the Astros built a strong data culture that permeates every facet of the team’s game strategy running the gamut from the process of player selection before the game to that of player position during the game. Such data-driven decisions would not have been possible without a data literate team.</p>
<h2 id="data-literacy-is-a-prerequisite-for-employees-to-interact-with-data-insights-meaningfully-and-critically-">Data literacy is a prerequisite for employees to interact with data insights meaningfully and critically.</h2>
<p>Data-literate employees are capable of creating and analyzing data visualizations that are integrated into the company&#39;s decision-making process. An example of such a company is PepsiCo, which uses <a target="_blank" href="https://www.datacamp.com/tracks/tableau-fundamentals">Tableau</a> and Hadoop to <a target="_blank" href="https://www.tableau.com/learn/webinars/how-pepsicos-big-data-strategy-disrupting-cpg-retail-analytics">visualize large volumes of data</a> that drive million-dollar sales decisions.</p>
<p>Moreover, being data literate is also a prerequisite to one’s ability to critically evaluate the validity of the data used to create visualizations.</p>
<blockquote>
<p>These employees who interrogate the reliability, correctness, and consistency of the data sources can potentially catch costly mistakes, thereby improving the company’s confidence and effectiveness in making data-driven decisions.</p>
</blockquote>
<h2 id="data-literacy-forms-the-foundation-of-effective-data-governance">Data literacy forms the foundation of effective data governance</h2>
<p><a target="_blank" href="https://www.datacamp.com/webinars/how-data-governance-enables-scalable-data-science">Data governance</a> is the set of policies, processes and organizational structure that define how the organization’s data is managed. It ensures that data is readily available, relevant to create value for the company, of high quality, and compliant with existing regulations. To build strong data governance, leadership must have basic data literacy to first understand the data context and needs of the organization before setting data policies.</p>
<p><a target="_blank" href="https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/designing-data-governance-that-delivers-value">McKinsey</a> suggested that the foundation for effective data governance involves a data organizational structure with three components:</p>
<ol><li>the data management office (DMO) in charge of defining policies and standards</li>
<li>domain leaders who set and execute domain-specific strategies</li>
<li>the data council that connects the domain leaders and DMO</li></ol>
<p>Conceivably, the leaders in each of these components should understand data processes sufficiently to set up unambiguous and fair data governance roadmaps. For instance, data-literate domain leaders who understand the value of the data can effectively work with the DMO to design and deploy an appropriate enterprise data lake for a domain.</p>
<h2 id="data-literacy-helps-companies-make-ethical-ai-decisions">Data literacy helps companies make ethical AI decisions</h2>
<p>Companies that deploy AI systems for real-world use cases face the risk of AI systems, such as <a target="_blank" href="https://arxiv.org/pdf/1606.06565.pdf">AI accidents</a>, breaches of data privacy, and <a target="_blank" href="https://hbr.org/2019/10/what-do-we-do-about-the-biases-in-ai">AI bias</a>. One way to mitigate such a risk is to engage technical and non-technical stakeholders to critically examine the processes and outputs of the data science system. Business stakeholders who are data literate play a crucial role in evaluating the risks of AI systems and ensuring that existing AI systems are fair and ethical.</p>
<p>For instance, as Facebook has come under intense scrutiny in recent years over its recommendation systems, the <a target="_blank" href="https://ai.facebook.com/blog/what-ai-fairness-in-practice-looks-like-at-facebook/">Facebook Responsible AI Initiative</a> was founded. It consists of a multidisciplinary team of not just data scientists, but also philosophers and social scientists. This team evaluates existing systems and determines ethical standards openly. These qualitative standards are then translated and implemented by data scientists as quantitative metrics. Such debates on AI risks are productive only when all the stakeholders are well-informed and have a minimum level of data literacy to weigh the cost and benefits of an AI system. Similarly, organizations looking to operationalize and govern AI need a multidisciplinary team of business and data experts that have a common data language.</p>
<h2 id="building-organizational-data-literacy">Building Organizational Data Literacy</h2>
<p>Clearly, inculcating a strong data literacy in all employees brings about a myriad of benefits. Data literacy programs take many forms, including existing skills initiatives, e-learning courses, or specialized classroom training. Unsurprisingly, designing in-house data literacy programmes is a non-trivial and long-drawn process that requires meticulous planning. This is whyDataCamp for Business provides an interactive learning platform for companies that need to upskill and reskill their people on data skills. With topics ranging from data literacy, and data science to data engineering and machine learning, over 1,600 companies trust DataCamp for Business to upskill their talent.</p>
<br><p><center><a target="_blank" href="https://www.datacamp.com/business/demo"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603718763/banner_style_2_m573ef.png" /></a></p>

https://www.datacamp.com/community/blog/why-data-literacy-is-important-for-your-team

In this blog post, learn why data literacy is important and why some companies aren't extracting value from their data like they should be.

Why Data Literacy Is Important for Your Team

https://www.datacamp.com/community/blog/creating-trust-in-data-with-data-observability

In this episode, Adel speaks with Barr Moses, CEO, and co-founder of Monte Carlo, on the importance of data quality and how data observability creates trust in data throughout the organization.

Creating Trust in Data with Data Observability

<p>Over the last few years, AI has seen significant growth in utilization and effectiveness in organizations. Its applications range from predictive analytics for more efficient and accurate business operations, to machine vision for direct interactions with the world, and natural language processing (NLP) for more effective interaction with technology through language. <a href="https://www.accenture.com/us-en/insights/artificial-intelligence/ai-investments">Accenture reports</a> that 84% of surveyed executives believe they need to leverage AI to achieve their growth objectives.</p>
<p>With this fast growth in a relatively new and maturing technology comes a lot of responsibility and ethical concerns. <a target="_blank" href="https://www.capgemini.com/news/ai-and-ethics-2020-report/">Capgemini found</a> that 70% of customers expect transparency and fairness in their AI interactions. Additionally, regulators are beginning to tighten regulations around AI transparency and fairness. The European Union recently released a set of AI regulations that aim to minimize the harmful impacts of AI systems, and the FTC announced plans to go after companies selling biased algorithms (<a target="_blank" href="https://www.technologyreview.com/2021/04/21/1023254/ftc-eu-ai-regulation-bias-algorithms-civil-rights/">MIT Tech Review</a>).</p>
<p>The increases in ethical concerns are justified and driven by AI use cases that increasingly involve highly sensitive settings such as healthcare, judicial systems, and finance. Without proper tuning and understanding of the potential ethical risks, AI can lead to the reproduction and amplification of historical bias and discrimination. For example, <a target="_blank" href="https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm">ProPublica investigated the COMPAS algorithm</a> which was used to inform sentencing for criminals by predicting the likelihood of an individual to re-offend. They discovered that black defendants were significantly more likely to receive false-positive recommendations suggesting they would re-offend when they would not while white defendants were more likely to receive false-negative recommendations suggesting they would not re-offend. </p>
<p>Similarly, OpenAI’s <a target="_blank" href="https://github.com/openai/gpt-3">GPT-3</a> NLP transformer model with 175 billion parameters has demonstrated significant bias in generating sentences that include <a target="_blank" href="https://arxiv.org/pdf/2101.05783v1.pdf">religion</a>. The model has also shown bias toward other groups, including gender and race which is discussed in the <a target="_blank" href="https://arxiv.org/pdf/2005.14165.pdf">original paper</a>.  </p>
<p>As AI becomes more ubiquitous, it’s crucial to ensure the responsible use of AI. If we are to trust these systems to inform important and sensitive decisions, they cannot be biased or include historical stereotypes.</p>
<h2 id="what-is-algorithmic-bias-">What is Algorithmic Bias?</h2>
<p>Algorithmic bias is pretty prominent in the news, as seen in <a target="_blank" href="https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G">gender bias</a> in hiring decisions favoring men, online advertisements that reproduce redlining on a map (<a target="_blank" href="https://www.theguardian.com/technology/2019/mar/28/facebook-ads-housing-discrimination-charges-us-government-hud">housing discrimination</a>), and healthcare algorithms that include <a target="_blank" href="https://science.sciencemag.org/content/366/6464/447">racial bias</a>. All of these examples clearly show a group being treated differently by the model than another group.</p>
<p>Many metrics exist for formally defining algorithmic bias. The basic definition is that an algorithm treats groups in the data differently. The groups we are concerned about, such as race, gender, religion, and pregnancy or veteran status are called protected or sensitive characteristics. When analyzing an algorithm, it’s necessary to understand what groups are potentially most vulnerable to harmful impacts of the model.</p>
<p>There are two categories of metrics to determine the bias and fairness of an algorithm: fairness by representation and fairness by error.</p>
<h3 id="fairness-by-representation">Fairness by Representation</h3>
<p>These metrics evaluate the outcomes of the model for different groups. For example, these metrics could look at the proportion of men who get called back for an interview versus women for a hiring algorithm.</p>
<h3 id="fairness-by-error">Fairness by Error</h3>
<p>Fairness by error metrics observe the difference in the proportion of error rates made for each group. For example, in the COMPAS algorithm bias described above, the false-positive rate for black defendants was significantly higher than other groups, and the false-negative rate for white defendants was significantly lower.</p>
<h3 id="causes-of-algorithmic-bias">Causes of Algorithmic Bias</h3>
<p>Bias in an algorithm is caused by the underlying data. Simple models like linear regression and more complex transformer models are not biased on their own; they produce outputs based on the data provided to them. Below are five causes of bias in data.</p>
<ol>
<li><strong>Skewed dataset:</strong> A dataset may have different proportions of data for each group or training examples for each group which may lead to biased model outputs.</li>
<li><strong>Tainted examples:</strong> The data may have unreliable labels or historical bias may exist in the data.</li>
<li><strong>Limited Features:</strong> The collected features may be unreliable or not informative for specific groups due to bad data collection practices.</li>
<li><strong>Samples Size:</strong> AI algorithms struggle to perform well with smaller datasets.</li>
<li><strong>Proxy Features:</strong> Some features can leak information about protected variables, even if the protected variable is not included in the data. <a target="_blank" href="https://arxiv.org/pdf/1707.08120.pdf">Research has shown</a> that variables like zip code, sports activity, and the university a person attended can all indirectly inform a model about protected variables which can lead to algorithmic bias.</li>
</ol>
<h2 id="what-are-mitigation-techniques-that-can-be-used-">What are mitigation techniques that can be used?</h2>
<p>We now have a good formal understanding of what algorithmic bias is and what it is caused by. There are two potential avenues for mitigating algorithmic bias: technical techniques and AI governance. Most likely a combination of these two techniques will yield the best results in reducing the presence of bias in an algorithm.</p>
<h3 id="technical-mitigation-techniques">Technical Mitigation Techniques</h3>
<p>Within technical techniques, there are three approaches: pre-processing techniques which reduce bias in the original data before training, in-processing methods that mitigate bias during model training, and post-processing algorithms that reduce bias by modifying predictions.</p>
<p><a target="_blank" href="https://core.ac.uk/download/pdf/81728147.pdf">This paper</a> describes four common pre-processing techniques used in the literature in the introduction:</p>
<ul>
<li><strong>Suppression:</strong> remove a variable that correlates most with a sensitive variable</li>
<li><strong>Massaging:</strong> Relabel some observations in the data in an efficient way using a ranker to remove discrimination in the input data</li>
<li><strong>Reweighting:</strong> Assign weights to each tuple in the training data to remove discrimination</li>
<li><strong>Sampling:</strong> Sample from each group to get a more balanced representation of each group using either uniform sampling or preferential sampling by duplicating or skipping observations.</li>
</ul>
<p>Other techniques like <a target="_blank" href="https://krvarshney.github.io/pubs/CalmonWVRV_nips2017.pdf">this paper’s</a> probabilistic formulation and <a target="_blank" href="http://sorelle.friedler.net/papers/kdd_disparate_impact.pdf">this paper</a>, which describes two “repair procedures,” provide good methods of removing bias as well in the pre-processing stage.</p>
<p>In-processing techniques focus on removing bias during training. These techniques typically include fairness constraints which impact the optimization problem and lead to more fair results. Examples discussing in-process methods include this one looking at <a target="_blank" href="https://arxiv.org/pdf/1806.06055.pdf">fairness constraints</a> and this one looking at <a target="_blank" href="https://www.kamishima.net/archive/2012-p-ecmlpkdd-print.pdf">fairness-aware classification</a>.</p>
<p>Finally, post-processing algorithms modify model outputs to reduce bias. These algorithms can either change predictions for privileged or unprivileged groups as seen by <a target="_blank" href="https://arxiv.org/pdf/1709.02012.pdf">Pleiss et al.</a> and <a target="_blank" href="https://arxiv.org/pdf/1610.02413.pdf">Hardt et al.</a> or they reduce bias when an algorithm output is close to the decision boundary (where it is less sure about the result) as seen in <a target="_blank" href="https://mine.kaust.edu.sa/Documents/papers/ICDM_2012.pdf">this paper</a> on decision theory.</p>
<p>For any of these techniques, it is essential to discuss with all stakeholders involved the trade-offs in accuracy and the risks being taken by changing the underlying data or model results. These techniques can lead to predicting outcomes that have not been seen historically or are not seen in the data.  </p>
<h3 id="governance-and-accountability">Governance and Accountability</h3>
<p>There are a lot of benefits to using AI in organizations. However, as seen throughout the blog post, ethical risks posed by AI systems lead to organizational risks such as reputational damage, which lead to loss of revenue. No firm wants to have a headline saying they used a biased algorithm to make business decisions that negatively impacted a group of customers or employees.</p>
<p>Proper AI governance is conducted through comprehensive risk management. The specific use case of the AI decision and the associated impact assessment for each stakeholder needs to be identified. Understanding the potential monetary and human risks associated with the use case is important. Once the magnitude of each risk is accurately assessed for each stakeholder, the likelihood for these repercussions should be calculated.</p>
<p>AI governance best practices include comprehensive checklists for stakeholders, automated and manual testing, business standards compliance testing, detailed reports, and assertions from individuals and groups who are directly responsible for the model. For more, details make sure to download our white paper.</p>
<h2 id="responsible-ai-relies-on-data-literacy">Responsible AI Relies on Data Literacy</h2>
<p>Scaling responsible AI in organizations requires the development of data literacy within the organization. Successful AI governance includes multiple stakeholders engaging with and adopting a single framework. Understanding the scope of projects, how they are deployed and governed, and their impact and potential risks is a requirement for responsible AI for all involved stakeholders. <a target="_blank" href="https://www.accenture.com/_acnmedia/PDF-92/Accenture-AFS-Responsible-AI.pdf">Accenture argues</a> that data literacy builds trust and allows for responsible AI by including diverse perspectives which lead to consistently better outcomes.</p>
<p>By increasing baseline data literacy in an organization, subject matter experts who do not consistently interact with AI systems can include their expertise in these systems and communicate with technical stakeholders more effectively. This improved communication leads to more trust throughout the organization for all stakeholders involved.</p>
<p>The important aspects of data literacy for promoting responsible AI in an organization include an understanding of the following areas: the data science and machine learning workflow, different data roles, the flow of data through an organization, the distinction between various types of AI systems, and evaluation metrics for machine learning models.</p>
<h2 id="increase-baseline-data-literacy-to-scale-responsible-ai">Increase Baseline Data Literacy to Scale Responsible AI</h2>
<p>Training all stakeholders in an organization is required to promote responsible AI. Avoiding the associated risks with algorithmic bias is necessary to allow an organization to successfully take advantage of the many benefits of leveraging AI in business contexts. Data literacy is the key to successful communication between stakeholders, trust within the organization, and diverse solutions to be explored for responsible AI. The EU is allocating €1.1 trillion to their <a target="_blank" href="https://ec.europa.eu/social/BlobServlet?docId=22832&amp;langId=en">Skills Agenda</a>, with a heavy emphasis on AI skills and data literacy for regulators, demonstrating the importance of these tools in organizations. Many programs teach the fundamentals of data literacy. For more details on how data literacy can fuel responsible AI — check out our latest white paper.  </p>
<br><p><center><a target="_blank" href="https://www.datacamp.com/business/demo"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603718763/banner_style_2_m573ef.png" /></a></p>


https://www.datacamp.com/community/blog/the-case-for-responsible-ai

We recently released a report co-written by DataRobot’s VP of Trusted AI Ted Kwartler, Global AI Ethicist Haniyeh Mahmoudian, and Managing Director of AI Ethics Sara Khatry. Here’s a run-down of what to expect.

The Case for Responsible AI

<p>The concept of data-driven organizations has been a staple of the technology industry. Tech giants like Amazon, Google, and Netflix operate with data at the core of their business model, and they epitomize how data can be leveraged to drive decision-making towards successful business growth.</p>
<p>With growing data availability and significant decreases in costs for data processing and storage, opportunities to harness data to solve business challenges have extended beyond the technology industry. Finance, healthcare, and insurance companies have recognized this and have embarked on data transformation journeys to establish competitive business advantages.</p>
<p>While leveraging cutting edge-machine learning techniques and bridging the data science talent gap remains top of mind, an equally crucial aspect of a data strategy is the creation of a data governance plan. This article aims to dive into the definition of data governance, as well as the tools and technology that can drive successful governance programs.</p>
<h2 id="what-is-data-governance-">What is Data Governance?</h2>
<p>In a <a target="_blank" href="https://www.datacamp.com/resources/webinars/how-data-governance-enables-scalable-data-science">recent webinar</a>, Aaren Stubberfield, Data Scientist at Microsoft, outlined the opportunities and best practices of data governance, where he started by addressing the fundamental question of what data governance is.
Data governance, as defined by the <a target="_blank" href="https://www.dama.org/">Data Management Association (DAMA)</a>, is the exercise of authority and control (<strong>planning</strong>, <strong>monitoring</strong>, and <strong>enforcement</strong>) over the management of data assets.</p>
<p><strong>(1) Planning:</strong>  Creating rules that datasets need to conform to (e.g. format standardization of date fields e.g. YYYY-MM-DD)</p>
<p><strong>(2) Monitoring:</strong> Measuring compliance to rules set in place in the Planning phase (e.g. tracking the percentage of ‘Date’ fields with missing values)</p>
<p><strong>(3) Enforcement:</strong> Remediation actions in the event of data rule breaches based on level of urgency e.g. immediate corrective action if critical data fields such as social security number are found to contain major discrepancies</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1623443176/survey_1_nyksns.png" /></center>


<h2 id="what-does-a-data-governance-framework-look-like-">What does a Data Governance Framework look like?</h2>
<p>There are different types of data governance frameworks out there, but they tend to have these four common elements: Data Policies and Procedures, Datasets, Data Stewards and Stakeholders, and Technology.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1623443176/survey_2_zuwgvr.png" /></center><br>

<p>Data stewards are the people who develop governance policies and procedures for the datasets in the organization. Data governance tools are vital enablers for the stewards to do their best work, and they form a key part of the governance framework.</p>
<h2 id="what-are-the-tools-and-technology-used-for-data-governance-">What are the Tools and Technology used for Data Governance?</h2>
<p>Data governance tools are necessary to automate governance operations and data stewardship efforts since the large volumes of data render it impossible for these activities to be done manually. They are also able to integrate with different IT products and datasets along with comprehensive data cataloging, thereby extending their capabilities across an organization’s entire data management system.
To meet the burgeoning demand for these tools, there has been a proliferation of data quality solutions in the market. A <a target="_blank" href="https://www.gartner.com/en/documents/3988016/magic-quadrant-for-data-quality-solutions">review by Gartner</a> analyzed the data governance market and came up with Magic Quadrants to classify vendors offering data governance solutions.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1623443176/survey_3_pxeg3p.png" /></center><br>

<p>Given the growing importance of data governance, numerous data management tools offer governance capabilities as part of their systems. SolutionsReview released the <a target="_blank" href="https://solutionsreview.com/data-management/data-management-vendor-map-a-guide-to-the-best-data-management-tools/&amp;sa=D&amp;source=editors&amp;ust=1622129720378000&amp;usg=AOvVaw0Sld2arQpyWTWtd6n2BAiX">Data Management Vendor Map</a>, where they classified data management solutions into <strong>three categories</strong>: Data Quality Tools, Master Data Management, and Data Management for Analytics.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1623443176/survey_4_vo8yu6.png" /></center><br>

<h4 id="-1-data-quality-tools">(1)   Data Quality Tools</h4>
<p>Data quality is defined as the overall utility of the data and its ability to be easily processed and analyzed for other purposes. In order to achieve and sustain good data quality, the governance tools need to perform key functions such as standardizing, profiling, parsing, cleansing, and monitoring. Besides keeping the data clean and well-organized, these tools are also expected to support data processing across the organization’s entire data system.
<p>Examples:<br>
(i)          <a target="_blank" href="https://www.talend.com/products/data-integrity-governance/">Talend</a><br>
Talend’s flagship product is the <strong>Talend Data Fabric</strong>, which includes its data integrity and governance capabilities as part of a single unified cloud platform for trusted data. This includes metadata management to data lineage, as well as collaborative data stewardship solutions.</p>
<p>(ii)          <a target="_blank" href="https://www.sas.com/en_us/software/data-quality.html">SAS</a><br>
The <strong>SAS Data Quality</strong> product provides data quality management capabilities across different kinds of databases and data architecture deployments. It includes the essential functions of data cleansing, entity resolution, as well as a unified web-based console to monitor data quality jobs.</p>
<p>(iii)             <a target="_blank" href="https://www.informatica.com/products/data-quality/informatica-data-quality.html">Informatica</a><br>
Informatica offers an expansive portfolio of data tools in various deployments. Their <strong>Informatica Data Quality</strong> product provides users with a rich set of data transformation capabilities, while also allowing them to build and review business rules without relying on IT.</p>
<h4 id="-2-master-data-management">(2)   Master Data Management</h4>
<p>Master data refers to the consistent and uniform collection of core company-wide data points and comprises key components such as customers, leads, suppliers, employees, accounts, and more. Underpinning an effective data quality control strategy is the setup of solid master data management. It is only then that the enterprise’s growing data assets (along with its metadata) can be properly structured and consolidated in a central repository to support optimal decision making.
<p>Examples<br>
(i)             <a target="_blank" href="https://www.ataccama.com/platform/master-data-management">Ataccama</a><br>
The <strong>Ataccama Platform</strong> product serves as an augmented data management platform that includes MDM as one of its modules. It also includes other relevant modules like data quality and metadata management, and is fully integrated for different types of deployment.</p>
<p>(ii)            <a target="_blank" href="https://www.winshuttle.com/products/enterworks/master-data-management/">Enterworks</a><br>
The <strong>EnterWorks Platform</strong> is a multi-domain platform that creates a central repository of reliable, up-to-date master data consolidated across all enterprise applications with powerful tools to improve data quality and governance. Furthermore, all administrative and governance functions across the multiple domains can be achieved from a single user interface.</p>
<p>(iii)          <a target="_blank" href="https://www.riversand.com/products/mdm/">Riversand</a><br>
Riversand’s <strong>Master Data Experience Management</strong> platform provides a multi-domain cloud-native, unified software-as-a-service (SaaS) platform for all MDM use cases. It aims to eliminate data silos and support compliance with data governance rules by creating a single, accurate, trustworthy source of master data, along with comprehensive views of business-critical data.</p>
<h4 id="-3-data-management-for-analytics">(3)   Data Management for Analytics</h4>
<p>With the multitude of governance activities involved in maintaining data quality, enterprises may want to look for comprehensive integrated data platforms instead of standalone solutions. Data management for analytics solutions are comprehensive systems that integrate with analytics software to oversee data analytics such as relational and non-relational analytical processing, business intelligence, and machine learning.
<p>Examples<br>
(i)             <a target="_blank" href="https://www.collibra.com/data-governance">Collibra</a>
The <strong>Collibra Data Governance</strong> product is a cloud-based platform that helps enterprises establish a common data asset understanding and collaborate in a central location. This includes a suite of services such as policy manager, reference data and business glossaries. All these help to generate trusted data for powerful business analytics to be built upon.</p>
<p>(ii)          <a target="_blank" href="https://erwin.com/solutions/metadata-management/">erwin</a>
The erwin Data Intelligence Suite (<strong>erwin DI</strong>) serves as a unified software platform that lets users create automated and curated enterprise data catalogs complete with data models and on-demand lineage. This drives agile and well governed data preparation and analytics, with integrated business glossaries for organization-wide data literacy.</p>
<p>(iii)            <a target="_blank" href="https://www.alation.com/product/platform/">Alation</a>
The <strong>Alation Platform</strong> empowers analysts and business users with an open and intelligent platform that supports a wide variety of metadata management applications to data catalog and governance. Furthermore, its integrated Analytics product serves as a one-stop shop for productive self-service analytics.</p>
<h2 id="how-to-maximize-the-value-of-these-tools-">How to maximize the value of these tools?</h2>
<p>It is important to remember that tools are just one lever in the strategy towards data democratization. To make full use of these data governance tools, the people in the organization are key.
This is because good data governance is a team effort that requires humans to design and implement the agenda and policies and then leverage technology to automate and monitor these procedures optimally.
To achieve this, organizations need to start focusing on data literacy upskilling so that everyone has the skills to work with data to do their best work.</p>
<p>Learn more about <a target="_blank" href="https://www.datacamp.com/groups/business">DataCamp for Business</a>:<br>
<i>DataCamp for Business provides an interactive learning platform for companies that need to upskill and reskill their people on data skills—on everything from data literacy and data science to data engineering and machine learning. Join 1,600+ companies and 80% of the Fortune 1000 who use DataCamp to upskill their teams.</i></p>
<br><p><center><a target="_blank" href="https://www.datacamp.com/business/demo"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603718763/banner_style_2_m573ef.png" /></a></p>


https://www.datacamp.com/community/blog/a-survey-into-data-governance-tools

In a recent webinar, we discussed how data governance is a key component of an organization’s data strategy and enables it to harness the full value of data. For a data governance plan to succeed, it is important that the right tools and technology are employed.

In a recent webinar, we discussed how data governance is a key component of an organization’s data strategy and enables it to harness the full value of data.

A Survey Into Data Governance Tools

<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1623167783/spotlight-iryna-datacamp_vjypef.png" /></center><br></p>
<p>Once an accountant in Ukraine, Iryna’s move to Belgium and a new culture inspired a fresh start. After learning web design from <a target="_blank" href="http://www.hackyourfuture.be/">Hack Your Future Belgium</a>, one of DataCamp Donates’ partner organizations, she now works with us full-time as a frontend developer. Read on to find out how, for one woman, data got democratized.</p>
<h4 id="let-s-start-with-the-present-what-do-you-do-at-datacamp-">Let’s start with the present: what do you do at DataCamp?</h4>
<p>I started last March as an intern in software development, and incidentally, today is my first day working in this role full-time. Right now, I work with the commercial team on all DataCamp’s signed-out pages: that is, when you visit the website all the pages you can reach from there without signing in is our work.</p>
<h4 id="what-does-your-job-typically-involve-">What does your job typically involve?</h4>
<p>I start my day by checking new tickets (requests from colleagues that relate to my job). Then we have our daily meeting—although this gets repeated later in the day because of the time gap between us, the London team, and the team in the US. Usually, I have a couple of other meetings with other team members to ask work-related questions—other than that, I press on with all the jobs on my plate.
Working from home was a bit strange at first since I started my internship completely remotely, so I saw two people from the office on my first day when I went in to pick up my laptop, and have never seen my teammates in real life since. Still, the communication and everything else works so well here at DataCamp that it never really feels lonely.</p>
<h4 id="going-back-in-time-where-are-you-from-">Going back in time, where are you from?</h4>
<p>I’m from Ukraine and I moved to Belgium one year ago. That actually inspired my career change. Originally in Ukraine I was an accountant, and I moved to Belgium since my husband was working there. When I moved here it was a new beginning, and I asked myself if I would like to transition into something else. In the end, I chose to transition to web development, and I’m really happy that I made that choice—I love this job, there’s so much variety to it, so much room to be creative and so much room for me to learn!</p>
<h4 id="where-did-your-interest-in-tech-start-">Where did your interest in tech start?</h4>
<p>When I moved I was kind of out of my depth. My English wasn’t perfect, I didn’t speak any Flemish or French, so when I went to an integration course to learn these languages I also had an opportunity to take courses in tech too—HackYourFuture ran one. They would later become one of DataCamp Donates’ main partners. They were offering tech courses to newcomers and refugees in Belgium for free—I didn’t really know if it was for me, but I decided to give it a try. I ended up spending the rest of the year in that course, and afterward decided to pursue it as a career. I came across the internship on DataCamp, and now that’s me working in development full-time! My first project, incidentally, was DataCamp Donates itself, when they were working on launching their own site. Effectively, my role was using Squarespace to make the site match the main site’s design.</p>
<h4 id="is-there-anything-you-ll-be-working-on-in-the-future-that-particularly-excites-you-">Is there anything you’ll be working on in the future that particularly excites you?</h4>
<p>I really like the fact that I work with DataCamp Donates to expand what they do to more countries. For me, that quite literally hits close to home. But every day there are new projects coming in, and the main thing that excites me is that I work with such amazing people from whom I can learn a lot.</p>
<h4 id="any-advice-for-how-datacamp-donates-can-better-itself-since-you-ve-experienced-what-they-offer-as-a-learner-">Any advice for how DataCamp Donates can better itself since you’ve experienced what they offer as a learner?</h4>
<p>Keep doing what you do. There are a lot of countries like, for example, Ukraine itself where development is beginning to take root. So, the education and lifestyle are great in the capital but in smaller cities and the countryside not so much. As a result people really have an interest in upgrading their skills, and parents are determined to ensure their children get the best education.</p>
<p><i>Would you like to join Iryna at DataCamp? We’re currently hiring in New York, London, Leuven, Belgium, and remote locations. Check out our <a target="_blank" href="https://www.datacamp.com/jobs">open roles</a>.</i></p>
<p><i>Hack Your Future Belgium is an open education nonprofit that prepares newcomers for a career in web development, with a special focus on refugees and women. They facilitate integration and address the shortage of qualified workforce in the IT sector. Learn more and get involved at <a target="_blank" href="https://hackyourfuture.be/">www.hackyourfuture.be</a>.</i></p>

https://www.datacamp.com/community/blog/new-hire-spotlight-iryna

Learn how a DataCamp Donates participant landed a job with DataCamp.

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