Schedule: Ethics sessions

Answering the simple question of what rights Indian citizens have over their data is a nightmare. The rollout of India Stack technology-based solutions has added fuel to fire. Sundeep Reddy Mallu explains, with on-the-ground examples, how businesses and citizens in India's booming digital economy are navigating the India Stack ecosystem while dealing with data privacy, security, and ethics. Read more.

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's a growing demand for fairness, accountability, and transparency from machine learning (ML) systems. Nick Pentreath explains how to build just such a pipeline leveraging open source tools. Read more.

As companies commercialize novel applications of AI in areas such as finance, hiring, and public policy, there's concern that these automated decision-making systems may unconsciously duplicate social biases, with unintended societal consequences. Laila Paszti shares practical advice for companies to counteract such prejudices through a legal- and ethics-based approach to innovation. Read more.

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.

Machine learning applications balance interpretability and performance. Linear models provide formulas to directly compare the influence of the input variables, while nonlinear algorithms produce more accurate models. Eitan Anzenberg explores a solution that utilizes what-if scenarios to calculate the marginal influence of features per prediction and compare with standardized methods such as LIME. Read more.

The success of machine learning algorithms in a wide range of domains has led to a desire to leverage their power in ever more areas. Maren Eckhoff discusses modern explainability techniques that increase the transparency of black box algorithms, drive adoption, and help manage ethical, legal, and business risks. Many of these methods can be applied to any model without limiting performance. Read more.

DataKind UK has been working in data for good since 2013, helping over 100 UK charities to do data science for the benefit of their users. Some of those projects have delivered above and beyond expectations; others haven't. Duncan Ross and Giselle Cory explain how to identify the right data for good projects and how this can act as a framework for avoiding the same problems across industry. Read more.

Machine learning (ML) 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 are big risk factors in ML. However, we creators have a lot of experience dealing with intelligent beings—one another. Jerry Overton uses this common sense to build a checklist for protecting against ethical violations with ML. Read more.