In previous work with Joost Vennekens I proposed a definition of actual causation that is based on certain plausible principles, thereby allowing the debate on causation to shift away from its heavy focus on examples towards a more systematic analysis. This paper contributes to that analysis in two ways. First, I show that our definition is in fact a formalization of Wright's famous NESS definition of causation combined with a counterfactual difference-making condition. This means that our definition integrates two highly influential approaches to causation that are claimed to stand in opposition to each other. Second, I modify our definition to offer a substantial improvement: I weaken the difference-making condition in such a way that it avoids the problematic analysis of cases of preemption. The resulting Counterfactual NESS definition of causation forms a natural compromise between counterfactual approaches and the NESS approach.

Ross Intelligence, an artificial intelligence startup for the legal industry, has filed a counterclaim against Thomson Reuters as part of an ongoing legal battle between the two companies. The counterclaim was filed just days after Ross Intelligence publicly announced it is shutting down operations. In a company blog post from December 11, Ross Intelligence's founders stated the startup's platform will no longer be operational as of January 31. "We have not abandoned our vision for access to justice through the use of technology. We will continue to fight the good fight."

Artificial intelligence (AI) is no longer the stuff of science fiction. AI determines what news you get served up on the internet. It plays a key role in online matchmaking, which is now the way most romantic couples get together. It will tell you how to get to your next meeting, and what time to leave home so you're not late. AI often appears both omniscient and neutral, but on closer inspection we find AI learns from and adopts human biases.

As we make tremendous advances in machine learning and artificial intelligence technosciences, there is a renewed understanding in the AI community that we must ensure that humans being are at the center of our deliberations so that we don't end in technology-induced dystopias. As strongly argued by Green in his book Smart Enough City, the incorporation of technology in city environs does not automatically translate into prosperity, wellbeing, urban livability, or social justice. There is a great need to deliberate on the future of the cities worth living and designing. There are philosophical and ethical questions involved along with various challenges that relate to the security, safety, and interpretability of AI algorithms that will form the technological bedrock of future cities. Several research institutes on human centered AI have been established at top international universities. Globally there are calls for technology to be made more humane and human-compatible. For example, Stuart Russell has a book called Human Compatible AI. The Center for Humane Technology advocates for regulators and technology companies to avoid business models and product features that contribute to social problems such as extremism, polarization, misinformation, and Internet addiction. In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical challenges to a successful deployment of AI or ML in human-centric applications, with a particular emphasis on the convergence of these challenges. We provide a detailed review of existing literature on these key challenges and analyze how one of these challenges may lead to others or help in solving other challenges. The paper also advises on the current limitations, pitfalls, and future directions of research in these domains, and how it can fill the current gaps and lead to better solutions.

These rules need to ensure that human dignity and human rights are respected and that AI systems are subject to meaningful human control, allowing humans to correct or disable them in case of unforeseen behaviour. Humans should therefore be identifiable and ultimately held responsible. MEPs agreed that lethal autonomous weapon systems (LAWS) should only be used as a last resort and be deemed lawful only if subject to human control, since it must be humans that decide between life and death. The text calls on the EU to take a leading role in promoting a global framework on the military use of AI, alongside the UN and the international community. The increased use of AI systems in public services, especially healthcare and justice, should not replace human contact or lead to discrimination, MEPs assert.

To gain an insight into these and other essential 2021 trends for businesses, Digital Journal caught up with Robert Prigge, CEO of Jumio. Addressing bias in AI algorithms will be a top priority causing guidelines to be rolled out for machine learning support of ethnicity for facial recognition. Prigge explains: "Enterprises are becoming increasingly concerned about demographic bias in AI algorithms (race, age, gender) and its effect on their brand and potential to raise legal issues. Evaluating how vendors address demographic bias will become a top priority when selecting identity proofing solutions in 2021." Prigge adds: "According to Gartner, more than 95 percent of RFPs for document-centric identity proofing (comparing a government-issued ID to a selfie) will contain clear requirements regarding minimizing demographic bias by 2022, an increase from fewer than 15 percent today. Organizations will increasingly need to have clear answers to organizations who want to know how a vendor's AI "black box" was built, where the data originated from and how representative the training data is to the broader population being served."

Everyone has heard of this recidivism prediction software used by American judges that penalizes African American populations and, more recently, the Apple Pay Card algorithm giving men a higher credit limit than women, despite equivalent incomes. These are examples of unintentional racist and sexist discrimination that have attracted mistrust and discredit on technological solutions designed to accelerate processes and, paradoxically, optimize decision-making by reducing the part of the subjectivity of any human arbitration. These algorithmic systems that we think of as "objective" actually have three points of weakness: On the one hand, the algorithms themselves: Most application developers do not use learning algorithms that they have personally created to measure. In open access, these generic algorithms have for the most part been developed by scientists whose priority is to validate the precision of their mathematical model and to avoid over-learning, and not to ensure the generalization in all fairness. Thus, not only were none of these algorithms designed with an explicit objective of non-discrimination, but they were developed by a singularly homogeneous population.

Sometimes, like a total eclipse, my two great passions--technology and reality TV--become one. It happened in November 2017, when, on the Wednesday before Thanksgiving, I cajoled then-Future Tense contributor Jacob Brogan into writing about the previous night's Teen Mom 2 episode. It grappled with a critical question: Is it OK to shoot down a drone flying above private property? The segment in question begins as (former) teen mom Jenelle prepares for her wedding to David, a very cool and normal dude who we will return to in a moment. David, stalking the property like an ornery bison, calls Jenelle, informing her that "some girls" were attempting to take pictures of the event before it began.

On Monday morning, organizers of NeurIPS, the largest annual gathering of AI researchers in the world, gave Best Paper awards to the authors of three pieces of research, including one detailing OpenAI's GPT-3 language model. The week also started with AI researchers refusing to review Google AI papers until grievances were resolved after firing Ethical AI team co-lead Timnit Gebru. Googlers describe it as an instance of "unprecedented research censorship," raising questions of corporate influence. According to one analysis, Google publishes more AI research than any other company or institution. Tension between corporate interests, human rights, ethics, and power could be seen at workshops throughout the week.

We live in a world where humans aren't the only ones that have rights. In the eyes of the law, artificial entities have a legal persona too. Corporations, partnerships or nation states also have the same rights and responsibility as human beings. With rapidly evolving technologies, is it time our legal system considered a similar status for artificial intelligence (AI) and robots? "AI is already impacting most aspects of our lives. Given its pervasiveness, how this technology is developed is raising profound legal and ethical questions that need to be addressed," says Julian David, chief executive of industry body techUK.