Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Schedule: Ethics sessions

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11:1511:55 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
The application of AI algorithms in domains such as criminal justice, credit scoring, and hiring holds unlimited promise. At the same time, it raises legitimate concerns about algorithmic fairness. There is a growing demand for fairness, accountability, and transparency from machine learning (ML) systems. In this talk we cover how to build just such a pipeline leveraging open source tools. Read more.
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12:0512:45 Wednesday, 1 May 2019
Law and Ethics, Strata Business Summit
Location: Capital Suite 10/11
Laila Paszti (GTC Law Group PC & Affiliates)
As companies commercialize novel applications of AI in areas such as finance, hiring, and public policy, there is concern that these automated decision-making systems may unconsciously duplicate social biases, with unintended societal consequences. This talk will provide practical advice for companies to counteract such prejudices through a legal and ethics based approach to innovation. Read more.
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14:0514:45 Wednesday, 1 May 2019
Law and Ethics, Strata Business Summit
Location: Capital Suite 10/11
Duncan Ross (TES Global), Francine Bennett (Mastodon C)
Being good is hard. Being evil is fun and gets you paid more. Once more Duncan Ross and Francine Bennett explore how to do high-impact evil with data and analysis (and possibly AI). Make the maximum (negative) impact on your friends, your business, and the world—or use this talk to avoid ethical dilemmas, develop ways to deal responsibly with data, or even do good. But that would be perverse. Read more.
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14:5515:35 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
Eitan Anzenberg (Flowcast AI)
Machine learning applications balance interpretability and performance. Linear models provide formulas to directly compare the influence of the input variables, while non-linear algorithms produce more accurate models. We utilize "what-if" scenarios to calculate the marginal influence of features per prediction and compare with standardized methods such as LIME. Read more.
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17:2518:05 Wednesday, 1 May 2019
Law and Ethics, Strata Business Summit
Location: Capital Suite 12
Duncan Ross (TES Global), Giselle Cory (DataKind)
DataKind UK has been working in data for good since 2013 working with over 100 uk charities, helping them to do data science for the benefit of their users. Some of those projects have delivered above and beyond expectations - others haven't. In this session Duncan and Giselle will talk about how to identify the right data for good projects... Read more.
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11:1511:55 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Expo Hall (Capital Hall N24)
Machine-learning algorithms are good at learning new behaviors, but bad at identifying when those behaviors are harmful or don’t make sense. Bias, ethics, and fairness is a big risk factor in Machine Learning (ML). We have a lot of experience dealing with intelligent beings—one another. In this talk, we use this common sense to build a checklist for protecting against ethical violations with ML. Read more.
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16:3517:15 Thursday, 2 May 2019
Law and Ethics, Strata Business Summit
Location: Capital Suite 12
Sundeep Reddy Mallu (Gramener Inc)
Answering simple question of what rights do Indian citizens have over their data is a nightmare. The rollout of India Stack technology based solutions has added fuel to fire. Sundeep explains, with on ground examples, how businesses and citizens are navigating the India Stack ecosystem while dealing with Data privacy, security & Ethics space in India's booming digital economy. Read more.