Difference between revisions of "Ethics"
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<b>Michael Kearns: Algorithmic Fairness, Privacy & Ethics | [[Creatives#Lex Fridman|Lex Fridman]] Podcast #50 | <b>Michael Kearns: Algorithmic Fairness, Privacy & Ethics | [[Creatives#Lex Fridman|Lex Fridman]] Podcast #50 | ||
− | </b><br>I really enjoyed this conversation with Michael. Here's the outline: 0:00 - Introduction 2:45 - Influence from literature and journalism 7:39 - Are most people good? 13:05 - Ethical algorithm 24:28 - Algorithmic fairness of groups vs individuals 33:36 - Fairness tradeoffs 46:29 - Facebook, social networks, and algorithmic ethics 58:04 - Machine learning 58:05 - Machine learning 59:19 - Algorithm that determines what is fair 1:01:25 - Computer scientists should think about ethics 1:05:59 - Algorithmic privacy 1:11:50 - Differential privacy 1:19:10 - Privacy by misinformation 1:22:31 - Privacy of data in society 1:27:49 - Game theory 1:29:40 - Nash equilibrium 1:30:35 - Machine learning and game theory 1:34:52 - Mutual assured destruction 1:36:56 - Algorithmic trading 1:44:09 - Pivotal moment in graduate school | + | </b><br>I really enjoyed this conversation with Michael. Here's the outline: 0:00 - Introduction 2:45 - Influence from literature and journalism 7:39 - Are most people good? 13:05 - Ethical algorithm 24:28 - Algorithmic fairness of groups vs individuals 33:36 - Fairness tradeoffs 46:29 - [[Meta|Facebook]], social networks, and algorithmic ethics 58:04 - Machine learning 58:05 - Machine learning 59:19 - Algorithm that determines what is fair 1:01:25 - Computer scientists should think about ethics 1:05:59 - Algorithmic privacy 1:11:50 - Differential privacy 1:19:10 - Privacy by misinformation 1:22:31 - Privacy of data in society 1:27:49 - Game theory 1:29:40 - Nash equilibrium 1:30:35 - Machine learning and game theory 1:34:52 - Mutual assured destruction 1:36:56 - Algorithmic trading 1:44:09 - Pivotal moment in graduate school |
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Revision as of 22:06, 8 February 2023
YouTube search... ...Google search
- Case Studies
- Risk, Compliance and Regulation
- Government Services
- Law
- Defense
- Responsible AI Champions Pilot | Department of Defense Joint Artificial Intelligence Center (JAIC) ...DoD AI Principles ...Themes ...Tactics
- Implementing Responsible Artificial Intelligence in the Department of Defense May 26, 2021
- Other Challenges in Artificial Intelligence
- Explainable / Interpretable AI
- Bias and Variances
- Privacy
- Partnership on AI brings together diverse, global voices to realize the promise of artificial intelligence
- Montreal AI Ethics Institute creating tangible and applied technical and policy research in the ethical, safe, and inclusive development of AI.
- Amazon joins Microsoft in calling for regulation of facial recognition tech | Saqib Shah - engadget
- The Internet needs new rules. Let’s start in these four areas. | Mark Zuckerberg
- How Big Tech funds the debate on AI ethics | Oscar Williams - NewStatesman and NS Tech
- Europe is making AI rules now to avoid a new tech crisis | Ivana Kottasová - CNN Business
- OECD members, including U.S., back guiding principles to make AI safer | Leigh Thomas - Reuters
- 3 Practical Solutions to Offset Automation’s Impact on Work | Moran Cerf, Ryan Burke and Scott Payne - Singularity Hub
- EU backs AI regulation while China and US favour technology | Siddharth Venkataramakrishnan - The Financial Times Limited
- Could tough new rules to regulate big tech backfire? | Harry de Quetteville & Matthew Field - The Telegraph
- Don’t let industry write the rules for AI | Yochai Benkler - Nature
- The Algorithmic Accountability Act of 2019: Taking the Right Steps Toward AI Success | Colin Priest - DataRobot
Leading institutes and companies have published a set of ethical standards for AI research Europe is making AI rules now to avoid a new tech crisis
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