The economics of smaller budgets and larger case numbers necessitates the use of AI in legal proceedings. We examine the concept of disparate impact and how biases in the training data lead to the search for fairer AI. This paper seeks to begin the discourse on what such an implementation would actually look like with a criticism of pre-processing methods in a legal context . We outline how pre-processing is used to correct biased data and then examine the legal implications of effectively changing cases in order to achieve a fairer outcome including the black box problem and the slow encroachment on legal precedent. Finally we present recommendations on how to avoid the pitfalls of pre-processed data with methods that either modify the classifier or correct the output in the final step.

How can we update data for a machine learning model after it has already trained on that data? In this paper, we introduce DART, a variant of random forests that supports adding and removing training data with minimal retraining. Data updates in DART are exact, meaning that adding or removing examples from a DART model yields exactly the same model as retraining from scratch on updated data. DART uses two techniques to make updates efficient. The first is to cache data statistics at each node and training data at each leaf, so that only the necessary subtrees are retrained. The second is to choose the split variable randomly at the upper levels of each tree, so that the choice is completely independent of the data and never needs to change. At the lower levels, split variables are chosen to greedily maximize a split criterion such as Gini index or mutual information. By adjusting the number of random-split levels, DART can trade off between more accurate predictions and more efficient updates. In experiments on ten real-world datasets and one synthetic dataset, we find that DART is orders of magnitude faster than retraining from scratch while sacrificing very little in terms of predictive performance.

As artificial intelligence assumes a more central role in countless aspects of business and society, so has the need for ensuring its responsible use. AI has dramatically improved financial performance, employee experience, and product and service quality for millions of customers and citizens, but it has also inflicted harm. AI systems have offered lower credit card limits to women than men despite similar financial profiles. Digital ads have demonstrated racial bias in housing and mortgage offers. Users have tricked chatbots into making offensive and racist comments.

After months of teasers and announcements, Lucid Motors will finally reveal its first all-electric luxury sedan, the Air, during a live stream on September 9. But of course, the day before the big reveal, a little bit of news has trickled out. Lucid Motors has previously alluded that it will offer a high-end variant of the Air. That flagship variant, called the Dream, is expected to cost $169,000 (or $161,500 after federal tax credits are accounted for), according to a report by Bloomberg. The report said Lucid will produce a Grand Touring variant that will be priced in the low $130,000s after federal tax credits, as well as a sub-$100,000 Touring model.

When it comes to AI based innovation, companies the world over are vying for competitive advantage. However, when it comes to gaining the upper hand, many have written off European companies solely on the basis of stricter privacy laws. But is this really the case? Or could a privacy focused attitude be the secret to winning the AI race? Michael Ingrassia, president and general counsel at Truata tells us more.

Plopping down on a mattress, primping in front of a mirror or sitting on a toilet: In coming years, any of these activities could generate the most intimate data about your health, via sensors, wearables, machine-learning algorithms and data-mining systems. Though they promise to make health care more personalized, our nonstop interactions with digital technologies and analytics are upending traditional notions of patient confidentiality. In the U.S., the core federal law restricting the use and protecting the disclosure of health data is the Health Insurance Portability and Accountability Act, or HIPAA. Congress enacted it in 1996, when much of the health system was paperbound and fax-reliant. The law's age is showing.

Over the past year, my company, AndPlus, has been implementing some of the learnings from the online MIT Executive Education course Artificial Intelligence: Implications for Business Strategy. While the course isn't necessarily targeted towards dgital tansformation agencies in particular, it's easy to glean valuable insight into the future of business strategy as a whole from the content. If you've been paying any attention at all to the world around you, you've probably noticed quite a bit of discussion, speculation, and hand-wringing about emerging technologies, such as robotics and artificial intelligence (AI). Much of what's been written in the popular press and on social media centers around big, scary questions, such as: Jokes aside, these questions are based largely (though not completely) on runaway imaginations and jumping to conclusions about what the future will hold for these emerging technologies. Yes, they are important questions, and they need to be addressed, but, in many cases, it's premature to design policy and legal frameworks around what might not be a problem, to begin with.

The side-effects of algorithms are perilous as it leads to discrimination and intrudes with privacy and Freedom Of Expression. Infact, it is the logic behind, the Artificial Intelligence. Right from getting recommendation about a particular gadget that we want to buy online to job applications online, algorithms play a major role. And as the competition amongst tech giants are improving, humans are presented with more cutting-edge technology that applies Artificial Intelligence. But algorithms are not new.

Portland, Oregon officials have passed what could be the strictest municipal ban on facial recognition in the country. That means places like hotels, stores and restaurants can't use facial recognition where customers will be present. According to CNET, the bill passed unanimously, and it will be enforced starting in January 2021. Businesses caught violating the law could be sued and could pay up to $1,000 a day in fines. In the document (PDF) detailing the ordinance, the city council noted that "Black, Indigenous and People of Color communities have been subject to over surveillance and disparate and detrimental impact of the misuse of surveillance."

The prevalence of machine learning models in various industries has led to growing demands for model interpretability and for the ability to provide meaningful recourse to users. For example, patients hoping to improve their diagnoses or loan applicants seeking to increase their chances of approval. Counterfactuals can help in this regard by identifying input perturbations that would result in more desirable prediction outcomes. Meaningful counterfactuals should be able to achieve the desired outcome, but also be realistic, actionable, and efficient to compute. Current approaches achieve desired outcomes with moderate actionability but are severely limited in terms of realism and latency. To tackle these limitations, we apply Generative Adversarial Nets (GANs) toward counterfactual search. We also introduce a novel Residual GAN (RGAN) that helps to improve counterfactual realism and actionability compared to regular GANs. The proposed CounteRGAN method utilizes an RGAN and a target classifier to produce counterfactuals capable of providing meaningful recourse. Evaluations on two popular datasets highlight how the CounteRGAN is able to overcome the limitations of existing methods, including latency improvements of >50x to >90,000x, making meaningful recourse available in real-time and applicable to a wide range of domains.