- Stop Blaming Humans for Bias in AI - Nov 19, 2021.
Can artificial intelligence be rid of bias? This is an important question, and it’s equally important that we look in the right place for the answer.
- The Case for a Global Responsible AI Framework - Oct 30, 2021.
Public and private organizations have come out with their own set of AI principles, focusing on AI-related risks from their perspective. However, it’s imperative d=to have a global consensus on Responsible AI – based on data governance, transparency and accountability – on how to utilize and benefit from AI in a way that is both consistent and ethical.
- Coding Ethics for AI & AIOps: Designing Responsible AI Systems - Aug 26, 2021.
AI ops has taken Human machine collaboration to the next level where humans and machines are not just coexisting but are collaborating and working together like team members.
- Demystifying AI: The prejudices of Artificial Intelligence (and human beings) - Aug 20, 2021.
AI models are necessarily trained on historical data from the real-world--data that is generated from the daily goings on of society. If social-based biases are inherent in the training data, then will the AI predictions highlight these same biases? If so, what should we do (or not do) about making AI fair?
- How to Create Unbiased Machine Learning Models - Jul 16, 2021.
In this post we discuss the concepts of bias and fairness in the Machine Learning world, and show how ML biases often reflect existing biases in society. Additionally, We discuss various methods for testing and enforcing fairness in ML models.
- Ethics, Fairness, and Bias in AI - Jun 30, 2021.
As more AI-enhanced applications seep into our daily lives and expand their reach to larger swaths of populations around the world, we must clearly understand the vulnerabilities trained machine leaning models can exhibit based on the data used during development. Such issues can negatively impact select groups of people, so addressing the ethical decisions made by AI--possibly unknowingly--is important to the long-term fairness and success of this new technology.
- How to easily check if your Machine Learning model is fair? - Dec 24, 2020.
Machine learning models deployed today -- as will many more in the future -- impact people and society directly. With that power and influence resting in the hands of Data Scientists and machine learning engineers, taking the time to evaluate and understand if model results are fair will become the linchpin for the future success of AI/ML solutions. These are critical considerations, and using a recently developed fairness module in the dalex Python package is a unified and accessible way to ensure your models remain fair.
- Navigate the road to Responsible AI - Dec 18, 2020.
Deploying AI ethically and responsibly will involve cross-functional team collaboration, new tools and processes, and proper support from key stakeholders.
- AI registers: finally, a tool to increase transparency in AI/ML - Dec 9, 2020.
Transparency, explainability, and trust are pressing topics in AI/ML today. While much has been written about why they are important and what you need to do, no tools have existed until now.
- When Machine Learning Knows Too Much About You - Nov 14, 2020.
If machine learning models predict personal information about you, even if it is unintentional, then what sort of ethical dilemma exists in that model? Where does the line need to be drawn? There have already been many such cases, some of which have become overblown folk lore while others are potentially serious overreaches of governments.
- Six Ethical Quandaries of Predictive Policing - Nov 6, 2020.
When predictive machine learning models are applied to real-life scenarios, especially those that directly impact humans, such as cancer detection and other medical-related applications, the risks involved with incorrect predictions carry very high stakes. These risks are also prominent in how machine learning is applied in law enforcement, and serious ethical questions must be considered.
- Overcoming the Racial Bias in AI - Oct 30, 2020.
The results of any AI developed today is entirely dependent on the data on which it trains. If the data is distributed--intentionally or not--with a bias toward any category of data over another, then the AI will display that bias. What is a better way forward to handle this possibility toward bias when the datasets involve human beings?
- Can AI Learn Human Values? - Oct 27, 2020.
OpenAI believes that the path to safe AI requires social sciences.
- The Ethics of AI - Oct 21, 2020.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about a very important subject - the ethics of AI.
- Goodhart’s Law for Data Science and what happens when a measure becomes a target? - Oct 14, 2020.
When developing analytics and algorithms to better understand a business target, unintended biases can sneak in that ensure desired outcomes are obtained. Guiding your work with multiple metrics in mind can help avoid such consequences of Goodhart's Law.
- Algorithms of Social Manipulation - Oct 9, 2020.
As we all continuously interact with each other and our favorite businesses through apps and websites, the level at which we are being tracked and monitored is significant. While the technologies behind these capabilities provide us value, the tech companies can also influence our decisions on where to click, spend our money, and much more.
- Data Science Minimum: 10 Essential Skills You Need to Know to Start Doing Data Science - Oct 1, 2020.
Data science is ever-evolving, so mastering its foundational technical and soft skills will help you be successful in a career as a Data Scientist, as well as pursue advance concepts, such as deep learning and artificial intelligence.
- Word Embedding Fairness Evaluation - Aug 5, 2020.
With word embeddings being such a crucial component of NLP, the reported social biases resulting from the training corpora could limit their application. The framework introduced here intends to measure the fairness in word embeddings to better understand these potential biases.
- Free From Stanford: Ethical and Social Issues in Natural Language Processing - Jul 17, 2020.
Perhaps it's time to take a look at this relatively new offering from Stanford, Ethical and Social Issues in Natural Language Processing (CS384), an advanced seminar course covering ethical and social issues in NLP.
- Google Open Sources TFCO to Help Build Fair Machine Learning Models - Mar 12, 2020.
A new optimization framework helps to incorporate fairness constraints in machine learning models.
- NLP Year in Review — 2019 - Jan 23, 2020.
In this blog post, I want to highlight some of the most important stories related to machine learning and NLP that I came across in 2019.
- Cartoon: Teaching Ethics to AI - Jan 4, 2020.
Ethics in AI has received significant attention recently, and the new KDnuggets cartoon examines the problem of teaching ethics to artificially intelligent entities.
- 5 Ways to Apply Ethics to AI - Dec 19, 2019.
Here are six more lessons based on real life examples that I think we should all remember as people working in machine learning, whether you’re a researcher, engineer, or a decision-maker.
- Dusting Under the Bed: Machine Learners’ Responsibility for the Future of Our Society - Dec 13, 2019.
The Machine Learning community must shape the world so that AI is built and implemented with a focus on the entire outcome for our society, and not just optimized for accuracy and/or profit.
- Ethical AI: EU’s New Guidelines and the Future of AI Trustworthiness - May 10, 2019.
The EU has issued a set of guidelines, "Ethics Guidelines for Trustworthy AI" to help tech companies steer towards ethical and inclusive AI as we come to terms with the potential of this technology.
- Designing Ethical Algorithms - Mar 8, 2019.
Ethical algorithm design is becoming a hot topic as machine learning becomes more widespread. But how do you make an algorithm ethical? Here are 5 suggestions to consider.
- OpenAI’s GPT-2: the model, the hype, and the controversy - Mar 4, 2019.
OpenAI recently released a very large language model called GPT-2. Controversially, they decided not to release the data or the parameters of their biggest model, citing concerns about potential abuse. Read this researcher's take on the issue.
- Reflections on the State of AI: 2018 - Feb 26, 2019.
We provide a detailed overview of the key developments in the AI space, focusing on key players, applications, opportunities, and challenges.
- Data Science and Ethics – Why Companies Need a new CEO (Chief Ethics Officer) - Jan 21, 2019.
We explain why data science companies need to have a Chief Ethics Officer and discuss their importance in tackling algorithm bias.
- The ultimate guide to starting AI - Nov 13, 2018.
A step-by-step overview of how to begin your project, including advice on how to craft a wise performance metric, setting up testing criteria to overcome human bias, and more.
- Ethics + Data Science: opinion by DJ Patil, former US Chief Data Scientist - Sep 14, 2018.
How much has data changed our lives over the past decade? Former US Chief Data Scientist DJ Patil investigates.
- The 2018 Data Scientist Report is Here - Aug 23, 2018.
Learn about the data and tools that data scientists are working with in 2018, Ethical issues around AI, Algorithmic bias, Job satisfaction, and more.
- Weapons of Math Destruction, Ethical Matrix, Nate Silver and more Highlights from the Data Science Leaders Summit - Jul 31, 2018.
Domino Data Lab hosted its first ever Data Science Leaders Summit at the lovely Yerba Buena Center for the Arts in San Francisco on May 30-31, 2018. Cathy O'Neil, Nate Silver, Cassie Kozyrkov and Eric Colson were some of the speakers at this event.
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- Deep Learning Summit, Toronto featuring Geoff Hinton – save with KDnuggets - May 29, 2018.
Geoffrey Hinton, one of the fathers of Deep Learning, will be back to share his most recent and cutting-edge research progressions, and will be joined by other top researchers. Save 20% on Early Bird passes when you sign up before 15 June w. code KDNUGGETS.
Also check Women in AI dinner series and get new white paper on Ethical implications of AI.
- Top 10 Technology Trends of 2018 - Apr 13, 2018.
In this article, we will focus on the modern trends that took off well on the market by the end of 2017 and discuss the major breakthroughs expected in 2018.
- 5 things that will be important in data science in 2018 - Jan 11, 2018.
What’s data science going to look like in 2018? How are job roles in the field going to change? Will AI find new ways to capture the public imagination? Learn more from Packt $5 books - on sale till Jan 16.
- Machine Ethics and Artificial Moral Agents - Nov 2, 2017.
This article is simply a stream of consciousness on questions and problems I have been thinking and asking myself, and hopefully, it will stimulate some discussion.
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- Strata Data Conference, NYC – Key Trends and Highlights - Oct 12, 2017.
Strata is a conference I very much enjoyed attending. This year, I observed a few common themes that ran across much of the conference content: Data Science Collaboration, Data Ethics, and Platform Optimization.
- Asimov’s 4th Law of Robotics - Sep 8, 2017.
It seems Isaac Asimov didn’t envision needing a law to govern robots in these sorts of life-and-death situations where it isn’t the life of the robot versus the life of a human in debate, but it’s a choice between the lives of multiple humans!
- The Surprising Ethics of Humans and Self-Driving Cars - Jan 9, 2017.
The surprising finding is that people are much more willing to ride in a self-driving car that might kill them to save several pedestrians than in a car that would save them but kill pedestrians. Asian respondents had significantly different preferences from US and Europe.
- Privacy, Security and Ethics in Process Mining - Dec 21, 2016.
Data Privacy, Security and Ethics are hot yet complex topics in the business and data science world. This important article talks about and provide guidelines for privacy, security and ethics, specifically in the context of Process Mining.
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- New Poll: Can You Live with Ethics of Machine Learning and Self-Driving Cars? - Dec 19, 2016.
The difficult thing about Machine Learning Ethics is that it forces us to consider the harsh choices people sometimes have to make but don't want to think about. Here is one such situation - what is the right choice? Please vote.
- Ethical Implications Of Industrialized Analytics - Nov 29, 2016.
Analytics & Big Data will be involved in every aspect of our lives and we should handle the ethical dilemmas wisely to let innovation contribute more to our lives.
- KDnuggets™ News 16:n36, Oct 12: Battle of the Data Science Venn Diagrams; 9 Bizarre and Surprising Insights; ROI in Big Data Analytics - Oct 12, 2016.
Battle of the Data Science Venn Diagrams; Top September Stories in KDnuggets; Open Images Dataset; Still Searching for ROI in Big Data Analytics?
- Humans & Machines Ethics Framework: Assessing Machine Learning Influence - Oct 11, 2016.
Humans & Machines Ethics Canvas’ main goal is to be a guide for critical thinking throughout the ethical decision-making process. It acts as a value system and an ethics framework to assess the influence of machine learning and software development while developing a system for individuals, teams, and organisations.
- 3 Key Ethics Principles for Big Data and Data Science - Jul 6, 2016.
If ethics in general are important, should ethics training be a crucial element of the data science field?
- Ethics in Machine Learning – Summary - Jun 6, 2016.
Still worried about the AI apocalypse? Here we are discussion about the constraints and ethics for the machine learning algorithms to prevent it.
- Ethics In Machine Learning: What we learned from Tay chatbot fiasco? - Mar 25, 2016.
As Microsoft chatbot Tay showed, Machine Learning brings us into a new world where our views on ethics and political correctness will be challenged. ML learns from us. In both good and bad ways, it reflects what we really are.
- Data Science and Prejudice – Blessing or Curse ? - Dec 23, 2015.
We examine the deep nature of bias and prejudice and wonder if prejudiced minds and 'good' data scientists coexist in harmony and if they can coexist, does it lead to disruption or disruptive innovation?
- Ethics should be a part of Data Science Training - Nov 6, 2015.
Over three quarters of Data Scientists support including ethics in Data Science training, and code of ethics is already a part of CAP certification and a part of UN Statistics division declaration.
- KDnuggets™ News 15:n34, Oct 21: How to Learn Machine Learning; Data Science Ethics? MetaMind MasterMind - Oct 21, 2015.
New Poll: Should Data Science Include Ethics Training?; The Best Advice From Quora on "How to Learn Machine Learning; Big Data + Wrong Method = Big Fail; MetaMind Mastermind Richard Socher: Uncut Interview;
- New Poll: Should Data Science Include Ethics Training? - Oct 20, 2015.
New poll is examining the question of Ethics and Data Science. Are Data Scientists more like physicians who take The Hippocratic Oath or more like Mathematicians, who work with numbers? Please vote.
- Lets talk about Ethics in Analytics / Data Science - Oct 20, 2015.
Is it time that data scientists go through formal ethics training? The saying “Lies, damned lies, and statistics” suggests that statistics (and Data Science) can be tweaked to prove any point and ethics training will help to improve the integrity and credibility of analytics profession.
- KDnuggets™ News 15:n16, May 20: 7 Techniques for Dimensionality Reduction; Who are the real Data Scientists? - May 20, 2015.
Seven Techniques for Data Dimensionality Reduction; Will the Real Data Scientists Please Stand Up. Most Viewed Data Mining Videos on YouTube; Should Data Science Really Do That?
- Should Data Science Really Do That? - May 13, 2015.
Data Science amazing progress in its ability to do predictions and analysis is raising important ethical questions, such as should that data be collected? Should the collected data be used for that application? Should you be involved?
- The Internet of People: 4 key principles for analyzing personal data - Feb 27, 2015.
The “Internet of People” raises a host of legal and ethical questions about the ownership and use of our personal data. We all have a stake in joining and navigating this increasingly stormy debate. Here are 4 key principles.
- Top KDnuggets tweets, Jun 11-12: Huge Big Data poster; “Data science” misses half the equation - Jun 13, 2014.
Huge Big Data Poster and Reference; "Data science" misses half the equation: you also need "decision science"; Proposed ethical guidelines for Twitter data mining: clear objectives, protect anonymity; Great talk at Google! John Ioannidis on why most published research is wrong.
- IEEE Rock Stars of Big Data Presentations - Jan 7, 2014.
This event, held at the Computer History Museum in Oct 2013, attracted a sold-out crowd who listened to 9 excellent speakers and leaders in the field - here are the presentations.