My opinions are my own. An introduction to the harm that ML systems cause and to the power imbalance that exists between ML system developers and ML system participants …and 10 concrete ways for machine learning practitioners to help build fairer ML systems. Image description: Photo of Black Lives Matter protesters in Washington, D.C. -- 2 signs say "Black Lives Matter" and "White Silence is Violence." Machine learning systems are increasingly used as tools of oppression. All too often, they're used in high-stakes processes without participants' consent and with no reasonable opportunity for participants to contest the system's decisions -- like when risk assessment systems are used by child welfare services to identify at-risk children; when a machine learning (or "ML") model decides who sees which online ads for employment, housing, or credit opportunities; or when facial recognition systems are used to surveil neighborhoods where Black and Brown people live. In reality though, machine learning systems reflect the beliefs and biases of those who design and develop them.

Following the launch of investigations last year, the U.S. Department of Justice (DOJ) together with attorney generals from 11 U.S. states filed a lawsuit against Google on Tuesday alleging that the company maintains monopolies in online search and advertising, and violates laws prohibiting anticompetitive business practices. It's the first antitrust lawsuit federal prosecutors filed against a tech company since the Department of Justice brought charges against Microsoft in the 1990s. "Back then, Google claimed Microsoft's practices were anticompetitive, and yet, now, Google deploys the same playbook to sustain its own monopolies," the complaint reads. "For the sake of American consumers, advertisers, and all companies now reliant on the internet economy, the time has come to stop Google's anticompetitive conduct and restore competition." Attorneys general from no Democratic states joined the suit.

Washburn Law's Robert J. Dole Center for Law and Government in partnership with the Washburn Law Journal is pleased to host "Artificial Rights?" The symposium will be held Thursday, November 5, 2020. It will be broadcast live via Zoom. Please register to receive the link for the Symposium. The symposium will explore the rights--and wrongs--of artificial intelligence (AI) and the extent to which AI has rights and can infringe rights.

After widespread protests against racism in the U.S., tech giants Microsoft, Amazon and IBM publicly announced they would no longer allow police departments access to their facial recognition technology. Artificial intelligence (AI) can be prone to errors, particularly in recognizing people of color and those in other underrepresented groups. Any organization developing or using AI solutions needs to be proactive in ensuring that AI dangers don't jeopardize their brand, draw regulatory actions, lead to boycotts or destroy business value. Microsoft President Brad Smith was widely quoted as saying his company wouldn't sell facial-recognition technology to police departments in the U.S., "until we have a national law in place, grounded in human rights, that will govern this technology." So, in the absence of highly rigorous institutional protections against AI dangers, what can organizations do themselves to guard against them?

In this study, we address the challenges in developing a deep learning-based automatic patent citation recommendation system. Although deep learning-based recommendation systems have exhibited outstanding performance in various domains (such as movies, products, and paper citations), their validity in patent citations has not been investigated, owing to the lack of a freely available high-quality dataset and relevant benchmark model. To solve these problems, we present a novel dataset called PatentNet that includes textual information and metadata for approximately 110,000 patents from the Google Big Query service. Further, we propose strong benchmark models considering the similarity of textual information and metadata (such as cooperative patent classification code). Compared with existing recommendation methods, the proposed benchmark method achieved a mean reciprocal rank of 0.2377 on the test set, whereas the existing state-of-the-art recommendation method achieved 0.2073.

A robust body of evidence demonstrates the adverse effects of implicit bias in various contexts--from hiring to health care. The Rooney Rule is an intervention developed to counter implicit bias and has been implemented in the private and public sectors. The Rooney Rule requires that a selection panel include at least one candidate from an underrepresented group in their shortlist of candidates. Recently, Kleinberg and Raghavan proposed a model of implicit bias and studied the effectiveness of the Rooney Rule when applied to a single selection decision. However, selection decisions often occur repeatedly over time. Further, it has been observed that, given consistent counterstereotypical feedback, implicit biases against underrepresented candidates can change. We consider a model of how a selection panel's implicit bias changes over time given their hiring decisions either with or without the Rooney Rule in place. Our main result is that, when the panel is constrained by the Rooney Rule, their implicit bias roughly reduces at a rate that is the inverse of the size of the shortlist--independent of the number of candidates, whereas without the Rooney Rule, the rate is inversely proportional to the number of candidates. Thus, when the number of candidates is much larger than the size of the shortlist, the Rooney Rule enables a faster reduction in implicit bias, providing an additional reason in favor of using it as a strategy to mitigate implicit bias. Towards empirically evaluating the long-term effect of the Rooney Rule in repeated selection decisions, we conduct an iterative candidate selection experiment on Amazon MTurk. We observe that, indeed, decision-makers subject to the Rooney Rule select more minority candidates in addition to those required by the rule itself than they would if no rule is in effect, and do so without considerably decreasing the utility of candidates selected.

Domains where supervised models are deployed often come with task-specific constraints, such as prior expert knowledge on the ground-truth function, or desiderata like safety and fairness. We introduce a novel probabilistic framework for reasoning with such constraints and formulate a prior that enables us to effectively incorporate them into Bayesian neural networks (BNNs), including a variant that can be amortized over tasks. The resulting Output-Constrained BNN (OC-BNN) is fully consistent with the Bayesian framework for uncertainty quantification and is amenable to black-box inference. Unlike typical BNN inference in uninterpretable parameter space, OC-BNNs widen the range of functional knowledge that can be incorporated, especially for model users without expertise in machine learning. We demonstrate the efficacy of OC-BNNs on real-world datasets, spanning multiple domains such as healthcare, criminal justice, and credit scoring.

How law regulates Artificial Intelligence (AI)? How do we ensure AI applications comply with existing legal rules and principles? Is new regulation needed and if yes, what type of regulation? These questions have gained increasing importance as AI deployment has increased across various sectors in our societies. Adopting new technological solutions has raised legislators' concern for the protection of fundamental rights both nationally in Finland and at the EU level. However, finding these answers is not easy. And the answers we find may be frustrating: varying from typical "it depends" to the self-evident "it's complicated", followed by the slightly more optimistic "we don't know yet".

It names smartwatches, TVs and connected cars, but voice searches are one of the fastest growing search areas right now. Most people are familiar with Amazon's Alexa's voice-assistant, which lets you ask questions instead of typing them in. Amazon is still the market leader in the smart speaker market, followed by Google with Apple trailing in third place. But those voice assistants are gaining users outside of speakers. Google's Assistant, for example, is already built into other products like Android and smartwatches as the default voice interface.

I've originally published this article here. In a previous post, I described why Artificial Intelligence (AI) is necessary for businesses and legal professionals who are reviewing legal documents. More people are relying on powerful Machine Learning models to streamline the document review process and make decisions in a fraction of the time. At SpeedLegal, we believe in products that are easy to use and accessible to everyone. Our motto is Answers to your legal concerns need to be two clicks away.