Move over Elon, someone else is stepping up to issue warnings about AI. AI and machine learning fears are often rooted in Terminator-style takeovers, jobs lost to automation and sentient technology out to undermine humanity. The reality of the technologies threats, however, may play out most dramatically in the technical legalese of state and federal regulations. A quick Google search shows that the United States does not have public policy relating to advanced technologies like AI and ML. Questions regarding the intellectual property rights of content produced by AI are currently playing out in discussion forums and professional meetings, but as of yet no significant AI or ML event or application has forced the country and legal system to reconcile the technology with the law.

Preserving the privacy of individuals by protecting their sensitive attributes is an important consideration during microdata release. However, it is equally important to preserve the quality or utility of the data for at least some targeted workloads. We propose a novel framework for privacy preservation based on the k-anonymity model that is ideally suited for workloads that require preserving the probability distribution of the quasi-identifier variables in the data. Our framework combines the principles of distribution-preserving quantization and k-member clustering, and we specialize it to two variants that respectively use intra-cluster and Gaussian dithering of cluster centers to achieve distribution preservation. We perform theoretical analysis of the proposed schemes in terms of distribution preservation, and describe their utility in workloads such as covariate shift and transfer learning where such a property is necessary. Using extensive experiments on real-world Medical Expenditure Panel Survey data, we demonstrate the merits of our algorithms over standard k-anonymization for a hallmark health care application where an insurance company wishes to understand the risk in entering a new market. Furthermore, by empirically quantifying the reidentification risk, we also show that the proposed approaches indeed maintain k-anonymity.

Replacing bank and insurance workers with machines risks creating a dependency on outside technology companies beyond the reach of regulators, the global Financial Stability Board (FSB) said on Wednesday. The FSB, which coordinates financial regulation across the Group of 20 Economies (G20), said in its first report on artificial intelligence (AI) and machine learning that the risks they pose need monitoring. AI and machine learning refer to technology that is replacing traditional methods to assess the creditworthiness of customers, to crunch data, price insurance contracts and spot profitable trades across markets. There are no international regulatory standards for AI and machine learning, but the FSB left open whether new rules are needed. Data on rapidly growing usage of AI is largely unavailable, leaving regulators unsure about the impact of potentially new and unexpected links between markets and banks, the report said.

Voters have a right to keep their political beliefs private. But according to some researchers, it won't be long before a computer program can accurately guess whether people are liberal or conservative in an instant. All that will be needed are photos of their faces. Michal Kosinski – the Stanford University professor who went viral last week for research suggesting that artificial intelligence (AI) can detect whether people are gay or straight based on photos – said sexual orientation was just one of many characteristics that algorithms would be able to predict through facial recognition. Using photos, AI will be able to identify people's political views, whether they have high IQs, whether they are predisposed to criminal behavior, whether they have specific personality traits and many other private, personal details that could carry huge social consequences, he said.

Despite my week-long Twitter consumption sabbatical (helped -- in part -- by the nigh week-long internet and power outage here in Maine), I still catch useful snippets from folks. My cow-orker @dabdine shunted a tweet by @terrencehart into a Slack channel this morning, and said tweet contained a link to this little gem. Said gem is the text of a very recent ruling from a District Court in Texas and deals with a favourite subject of mine: robots.txt. The background of the case is that there were two parties who both ran websites for oil and gas professionals that include job postings. One party filed a lawsuit against the other asserting that the they hacked into their system and accessed and used various information in violation of the Computer Fraud and Abuse Act (CFAA), the Stored Wire and Electronic Communications and Transactional Records Access Act (SWECTRA), the Racketeer Influenced and Corrupt Organizations Act (RICO), the Texas Harmful Access by Computer Act (THACA), the Texas Theft Liability Act (TTLA), and the Texas Uniform Trade Secrets Act (TUTS).

Can you tell us a little bit about your background and why you set up Artificial Lawyer? About 10 years ago, I started working at a large US consultancy as a strategy consultant to law firms. I then worked for a smaller boutique consultancy in the City of London. Two years ago, I set up my own business, Tromans Consulting, and I've been advising law firms on strategy and business decisions since. It became quite clear to me about 18 months ago that AI was going to have a profound impact as it evolves and spreads through the market, and I needed to take this into account when advising my clients.

There are now less than twenty seats left for next month's inaugural Legal Tech Summit in Sydney. With over 250 legal professionals registered to date, Australasian Lawyer readers planning to attend are encouraged to book their seat now to avoid disappointment. There are two ticket types available – a Tech Talks Pass priced at just $145 that provides access to a series of 20 minute bite-sized talks presented by industry experts that will cover a variety of topics from AI to data security, cost-effective tech solutions and more. Alternatively, delegates can upgrade to a full Conference Pass which includes access to the main conference program in addition to the Tech Talks. The full agenda for the event is available online.

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.

As Apple fans worldwide make lines outside stores to purchase the new iPhone X, the device's Face ID feature is being scrutinized by advocacy groups. The American Civil Liberties Union and the Center for Democracy and Technology told Reuters their concerns on whether Apple can enforce privacy rules for the iPhone X's facial recognition technology. The Face ID feature works to unlock the device, confirm Apple Pay payments, use Animoji and much more. It will also work with third-party apps. Face ID runs through the iPhone X's TrueDepth camera system, which maps the user's face with 30,000 infrared dots.

I was surprised to hear that a robot named Sophia was granted citizenship by the Kingdom of Saudi Arabia. The announcement last week followed the Kingdom's commitment of US$500 billion to build a new city powered by robotics and renewables. One of the most honourable concepts for a human being, to be a citizen and all that brings with it, has been given to a machine. As a professor who works daily on making AI and autonomous systems more trustworthy, I don't believe human society is ready yet for citizen robots. To grant a robot citizenship is a declaration of trust in a technology that I believe is not yet trustworthy.