Last year, when Kate Brodock, CEO and founder of Women 2.0, pointed out that one of the key problems faced in the new tech industry the world over was that it was dominated by'white men'. Lack of racial, cultural diversity and most importantly, lack of gender equality was shaping the internet in a skewed manner. The scant number of women in new tech, especially in the areas of data science, analytics, and artificial intelligence has been a worrying trend in India as well. The resultant sexism is increasingly becoming one of the side-effects, making the global protests for gender equality so much more necessary. Anuradha Sharma, chief operating officer at Scienaptic says, "There is a very prominent male culture in programming and that kind of makes women less visible in data science and analytics field… Women in analytics are hidden, they are doing all the good work, but they are not asserting themselves. I know enough number of women in analytics, who are doing great work but they are not visible."

One of the biggest issues facing the introduction of self-driving cars is where blamed is placed if one is involved in an accident. Does the responsibility lie with the vehicle owner, the car manufacturer or the firm that's developed the autonomous driving software? This and more will be decided in the next three years, with the Government launching a new legal review to prepare the country for driverless cars hitting UK roads. Minsters have commissioned the study in order to'ensure the UK remains one of the best places in the world to develop, test and drive self-driving vehicles'. Roads minister Jesse Norman yesterday announced the start of the review by the Law Commission of England and Wales and the Scottish Law Commission that will examine any legal obstacles that might restrict the widespread introduction of self-driving vehicles and highlight the need for regulatory reforms.

Cue the sad tuba and attorney jokes: Machines just landed the hurt on lawyers. LawGeex, an Israel-based startup focused on automating contract reviews, released a study showing its AI software pummels lawyers in document review accuracy. The AI service outperformed 20 corporate lawyers at identifying legal risks in nondisclosure agreement contracts. But don't worry, the machines got no papercuts. Undisclosed, however, is whether the lawyers involved in the study have sent their billable hours invoices to the machines for payment.

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 is 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.

We present a new approach for mitigating unfairness in learned classifiers. In particular, we focus on binary classification tasks over individuals from two populations, where, as our criterion for fairness, we wish to achieve similar false positive rates in both populations, and similar false negative rates in both populations. As a proof of concept, we implement our approach and empirically evaluate its ability to achieve both fairness and accuracy, using datasets from the fields of criminal risk assessment, credit, lending, and college admissions.

The Electronic Frontier Foundation has obtained documents showing that the FBI and Best Buy's Geek Squad have maintained a rather cozy relationship over the past decade. Memos and other records released through FOIA requests confirm reports from last year of Geek Squad members serving as paid FBI informants. They also reveal new details about how closely federal agents and Geeks communicated with one another. We first learned of a possible working relationship between the two groups last April during the child pornography trial of a gynecological oncologist named Mark Rettenmaier in California, who had taken his computer in for a repair at Best Buy. The customer service representatives there sent the computer to the main Geek Squad City facility in Brooks, Kentucky, where technicians discovered evidence of child porn and reported it to the FBI.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. Were you among the many Facebook users who received a notification introducing the platform's facial-recognition features last week? That's because, after years of using the software, lawsuits have finally pressured the social network into being more transparent about how it collects and employs this biometric data. Slate's Jaime Dunaway explains what the features mean for you and your privacy. For centuries, creators promised earlier versions of the tech (bertillonage anyone?) would bring about a golden era of accuracy and objectivity in identification.

On this week's If Then, Slate's April Glaser and Will Oremus try to make sense of Twitter CEO Jack Dorsey's rare honest assessment of his company's shortfalls, and what new state regulations mean for self-driving cars and trucks. Cody Wilson, the man behind the first 3-D–printed gun, joins the hosts to talk about his vision of a "WikiLeaks for guns" and why he thinks gun control is no longer possible. And as always, Don't Close My Tabs: This week Will looks at the "deepfakes" video phenomenon and April discusses former Trump aide Sam Nunberg's email inbox exhaustion.

While we hope it will not be lethal, the increasing use of artificial intelligenceArtificial Intelligence knows many different definitions, but in general it can be defined as a machine completing complex tasks intelligently, meaning that it mirrors human intelligence and evolves with time. in the legal services industry poses its own challenges. Law firms accustomed to using lawyers to perform certain tasks are now encountering technology, including artificial intelligence, that can perform tasks in seconds or minutes rather than the hours spent by a human counterpart. Although there are a growing number of firms using alternative fee arrangements, the majority of law firms continue to rely upon the billable hour as the source of their revenues. As technology and artificial intelligence continue to improve and threaten the traditional revenue model, law firms must assess how to use these technologies and consider other means of billing. What exactly is artificial intelligence?

The vegetables you eat with your Sunday roast may soon be picked by a robot. Farmers in Cornwall are testing a machine invented using European funding that picks cauliflowers from the field without bruising them. It works in a similar way to the human hand by squeezing each cauliflower before deciding whether it is ready to be harvested. The GummiArm robot is believed to be a answer to any migrant staff shortages that may arise when the UK leaves the EU. A cauliflower picking robot has been developed which can tell when the vegetable is ready for harvest and pull it out of the ground without damaging it.