Researchers in the US have used machine learning techniques to study the GDPR privacy policies of over a thousand representative websites based in the EU. They found that 97% of the sites studied failed to comply with at least one requirement of the European Union's 2018 regulatory framework, and that they complied least of all with regulatory requirements around the practice of'user profiling'. '[Since] the privacy policy is the essential communication channel for users to understand and control their privacy, many companies updated their privacy policies after GDPR was enforced. However, most privacy policies are verbose, full of jargon, and vaguely describe companies' data practices and users' rights. Therefore, it is unclear if they comply with GDPR.' 'Our results show that even after GDPR went into effect, 97% of websites still fail to comply with at least one requirement of GDPR.'

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research.

Manufacturers are frequently facing different challenges such as unexpected machinery failure or defective product delivery. Leveraging AI and machine learning, manufacturers can improve operational efficiency, launch new products, customize product designs and plan future financial actions to progress on their AI transformation. A recent MIT survey revealed that 60% of manufacturers are using AI to improve product quality, achieve greater speed and visibility across supply chain, and optimize inventory management. Implementing AI in manufacturing facilities is getting popular among manufacturers. According to Capgemini's research, more than half of the European manufacturers (51%) are implementing AI solutions, with Japan (30%) and the US (28%) following in second and third.

In September 2021, there was a panel at a ForHumanity conference, with senior guests from the US Equal Employment Opportunity Commission (EEOC), the US Government and Accountability Office, the European Commission and the UK Accreditation Service. The topic was AI-specific regulation, whether it is needed, the progress it is making and the implementation complexities. Paul Nemitz, from the European Commission, outlined the need for the proposed AI Act in the EU. Whilst GDPR regulates automated decision-making, it is focused on the use of personal data, rather than the technologies themselves. In Paul's opinion this leaves a gap, and the Act is expected to pass early in 2022.

Explainable artificial intelligence (XAI) and interpretable machine learning (IML) have become active research fields in recent years (Adadi and Berrada 2018; Molnar 2019). This is a natural consequence as complex machine learning (ML) models are now applied to solve supervised learning problems in many high-risk areas: cancer prognosis (Kourou et al. 2015), credit scoring (Kvamme et al. 2018), and money laundering detection (Jullum, Løland, et al. 2020). The high prediction accuracy of complex ML models often comes at the expense of model interpretability. As the goal of science is to gain knowledge from the collected data, the use of black-box models hinders the understanding of the underlying relationship between the features and the response, and thereby curtail scientific discovery. Model explanation frameworks from the XAI field extract the hidden knowledge about the underlying data structure captured by a black-box model, and thereby make the model's decision-making process transparent. This is crucial for, e.g., medical researchers that apply an ML model to obtain well-performing predictions, but who simultaneously also strive to discover important risk factors. Another driving factor is the Right to Explanation legislation in EU's General Data Protection Regulation (GDPR) (European Commission 2016).

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself.

If you want to automate as much of your anti-money laundering (AML) processes as possible to end the painful cycle of hiring and firing, or you're aware your current AML controls are not dynamic and explainable and it's finally time to do something about it, this blog post is for you. First off, digitally transforming a compliance function can sometimes seem like attempting to service a Boeing 747 engine at 35,000ft. The reality couldn't be more different. New platform technologies, designed for non-technical business users, enable any bank to easily implement transformative change to the automation of their AML processes. And getting set up can be achieved in a matter of weeks.

November 24, 2021 - The Federal Trade Commission has issued limited guidance in the area of artificial intelligence and machine learning (AI), but through its enforcement actions and press releases has made clear its view that AI may pose issues that run afoul of the FTC Act's prohibition against unfair and deceptive trade practices. In recent years it has pursued enforcement actions involving automated decision-making and results generated by computer algorithms and formulas, which are some common uses of AI in the financial sector but may also be relevant in other contexts such as health care. In FTC v. CompuCredit Corp., FTC Case No. 108-CV-1976 (2008), the FTC alleged that subprime credit marketer CompuCredit violated the FTC Act by deceptively failing to disclose that it used a behavioral scoring model to reduce consumers' credit limits. If cardholders used their credit cards for cash advances or to make payments at certain venues, such as bars, nightclubs and massage parlors, their credit limit might be reduced. The company, the FTC alleged, did not inform consumers that these purchases could reduce their credit limit, neither at the time they signed up nor at the time they reduced the credit limit.

Rapid advances in artificial intelligence ("AI") are unlocking enhanced capabilities for machine learning, data interpretation and innovation, whilst also increasingly becoming useful in our everyday lives. AI now plays a key role in drug discovery, the advertisements we see recommended to us online, route suggestions for online mapping platforms, and auto-generated digital content. Recently, this has raised questions for traditional thinking around intellectual property law, with particular implications for patent ownership and invention. The question is – could AI be capable of being considered an inventor? An additional step, that an inventor must be human, was recently put to the test.

Researchers at an artificial intelligence lab in Seattle called the Allen Institute for AI unveiled new technology last month that was designed to make moral judgments. They called it Delphi, after the religious oracle consulted by the ancient Greeks. Anyone could visit the Delphi website and ask for an ethical decree. Joseph Austerweil, a psychologist at the University of Wisconsin-Madison, tested the technology using a few simple scenarios. When he asked if he should kill one person to save another, Delphi said he shouldn't. When he asked if it was right to kill one person to save 100 others, it said he should.