The machine learning community has become increasingly concerned with the potential for bias and discrimination in predictive models. This has motivated a growing line of work on what it means for a classification procedure to be "fair." In this paper, we investigate the tension between minimizing error disparity across different population groups while maintaining calibrated probability estimates. We show that calibration is compatible only with a single error constraint (i.e. equal false-negatives rates across groups), and show that any algorithm that satisfies this relaxation is no better than randomizing a percentage of predictions for an existing classifier. These unsettling findings, which extend and generalize existing results, are empirically confirmed on several datasets.

We study the question of fair clustering under the {\em disparate impact} doctrine, where each protected class must have approximately equal representation in every cluster. We formulate the fair clustering problem under both the k-center and the k-median objectives, and show that even with two protected classes the problem is challenging, as the optimum solution can violate common conventions---for instance a point may no longer be assigned to its nearest cluster center! En route we introduce the concept of fairlets, which are minimal sets that satisfy fair representation while approximately preserving the clustering objective. We show that any fair clustering problem can be decomposed into first finding good fairlets, and then using existing machinery for traditional clustering algorithms. While finding good fairlets can be NP-hard, we proceed to obtain efficient approximation algorithms based on minimum cost flow. We empirically demonstrate the \emph{price of fairness} by quantifying the value of fair clustering on real-world datasets with sensitive attributes.

Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a particular race, gender, or sexual orientation. Since this past data may be biased, machine learning predictors must account for this to avoid perpetuating or creating discriminatory practices. In this paper, we develop a framework for modeling fairness using tools from causal inference. Our definition of counterfactual fairness captures the intuition that a decision is fair towards an individual if it the same in (a) the actual world and (b) a counterfactual world where the individual belonged to a different demographic group. We demonstrate our framework on a real-world problem of fair prediction of success in law school.

The adoption of automated, data-driven decision making in an ever expanding range of applications has raised concerns about its potential unfairness towards certain social groups. In this context, a number of recent studies have focused on defining, detecting, and removing unfairness from data-driven decision systems. However, the existing notions of fairness, based on parity (equality) in treatment or outcomes for different social groups, tend to be quite stringent, limiting the overall decision making accuracy. In this paper, we draw inspiration from the fair-division and envy-freeness literature in economics and game theory and propose preference-based notions of fairness -- given the choice between various sets of decision treatments or outcomes, any group of users would collectively prefer its treatment or outcomes, regardless of the (dis)parity as compared to the other groups. Then, we introduce tractable proxies to design margin-based classifiers that satisfy these preference-based notions of fairness. Finally, we experiment with a variety of synthetic and real-world datasets and show that preference-based fairness allows for greater decision accuracy than parity-based fairness.

WHEN the first printed books with illustrations started to appear in the 1470s in the German city of Augsburg, wood engravers rose up in protest. Worried about their jobs, they literally stopped the presses. In fact, their skills turned out to be in higher demand than before: somebody had to illustrate the growing number of books. Fears about the impact of technology on jobs have resurfaced periodically ever since. The latest bout of anxiety concerns the arrival of artificial intelligence (AI).

This blogpost is a round up of the various sets of principles of robotics and AI that have been proposed to date, ordered by date of first publication. The principles are presented here (in full or abridged) with notes and references but without commentary. If there any (prominent) ones I've missed please let me know. Asimov's three laws of Robotics (1950) A robot may not injure a human being or, through inaction, allow a human being to come to harm. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws. I have included these to explicitly acknowledge, firstly, that Asimov undoubtedly established the principle that robots (and by extension AIs) should be governed by principles, and secondly that many subsequent principles have been drafted as a direct response. The three laws first appeared in Asimov's short story Runaround [1]. This wikipedia article provides a very good account of the three laws and their many (fictional) extensions. A robot must respond to humans as appropriate for their roles.

As per the National Judicial Data Grid, over 26 Mn cases are pending across all the Local, District and High Courts and the Hon'ble Supreme Court of India and close to 9% of these cases are pending over 10 years or more[1]. On average 30,000 cases are filed every day and roughly 28,000 cases are adjudicated daily.[1] This means that there is a shortfall of 2,000 cases that are undecided, leading to a backlog of 7.3 lakh cases being added to the total cumulative backlog every year. The backlog of cases falls within the purview of the administrative function of the judiciary. The solution to this seemingly perennial problem also involves an exponential increase in Executive funding for judicial infrastructure and court expansion.

According to a report by Tata Consultancy Services, 68% of Indian companies use artificial intelligence (AI) for IT functions, but 70% believe AI's greatest impact will be in functions outside of IT such as marketing, customer service, finance and HR by 2020. Also, the majority of companies see AI as transformative and consider it crucial to remaining competitive in future. The primary goal of all AI-enabled innovation is to minimise human labour and augment human capability to the maximum extent possible. Machine learning (ML), a subset of AI, has been growing since the last 20 years but has hit an inflection point and evolved into the intersection of AI techniques and business intelligence (BI) analytics. Primarily due to acceleration in hardware and software capabilities, AI systems can now learn faster, predict with more accuracy and perform tasks that they haven't tried yet.

One of the most important issues that Congress will face in 2018 is how and when to regulate our growing dependence on artificial intelligence (AI). During the U.S. National Governors Association summer meetings, Elon Musk urged the group to push forward with regulation "before it's too late," stating that AI was an "existential threat to humanity." Hyperbole aside, there are legitimate concerns about the technology and its use. But a rush to regulation could exacerbate current issues, or create new issues that we're not prepared to deal with along the way. To begin with, one of the biggest issues in the world of AI is the lack of clear definition for what the technology is -- and is not.

Tom Martin, the founder of legal bot maker, LawDroid, has been awarded a contract to build a voice-activated legal aid bot in the US in a major'real world' test of the technology and its access to justice (A2J) capabilities. Martin told Artificial Lawyer that it will be the first chat bot/legal bot funded by the Legal Services Corporation's Technology Initiative Grant Program. In this case, the bot will be used on the HELP4TN web portal created by the Tennessee Alliance for Legal Services (TALS) in partnership with West Tennessee Legal Services. LawDroid beat out four other bot developers to win the mandate. The bot will operate off Martin's'Larissa' voice-activated bot platform, which Artificial Lawyer profiled last month.