The societal and ethical implications of the use of opaque artificial intelligence systems for consequential decisions, such as welfare allocation and criminal justice, have generated a lively debate among multiple stakeholder groups, including computer scientists, ethicists, social scientists, policy makers, and end users. However, the lack of a common language or a multi-dimensional framework to appropriately bridge the technical, epistemic, and normative aspects of this debate prevents the discussion from being as productive as it could be. Drawing on the philosophical literature on the nature and value of explanations, this paper offers a multi-faceted framework that brings more conceptual precision to the present debate by (1) identifying the types of explanations that are most pertinent to artificial intelligence predictions, (2) recognizing the relevance and importance of social and ethical values for the evaluation of these explanations, and (3) demonstrating the importance of these explanations for incorporating a diversified approach to improving the design of truthful algorithmic ecosystems. The proposed philosophical framework thus lays the groundwork for establishing a pertinent connection between the technical and ethical aspects of artificial intelligence systems.

GCHQ published a paper called Ethics of AI: Pioneering a New National Security which explains why the technology – enabling problem-solving at scale and speed – will be at the heart of our mission to keep the country safe in an increasingly complex world. The paper also details how GCHQ will ensure they use AI fairly and transparently, applying existing tests of necessity and proportionality. This includes establishing an AI ethical code of practice to recruiting more diverse talent to help develop and govern our use of AI, protecting privacy and striving for systematic fairness. Source: GCHQ to use AI to tackle child sex abuse, disinformation… – GCHQ.GOV.UK

Give us Innovative Puzzle and we will solve that puzzle. Intellectual Property Right scenario is to work in holistic view for knowledge exchange and visioning a world to solve massive issues which needs urgent attention to increase GDP of India aka Bharat. Are you looking for Chief Innovation Officier? Are you looking for Invention Harvesting and Invention Management? Doing international patent information researches, Patent Mapping to products to identify infringers, Intellectual Property Rights management for universities, drafting patent specifications, preparing response to notification of reasons for innovation refusal, go to person to facilitate patent licensing and initiating patent infringement proceedings before the Delhi High Court.

Over the last decade, we have heard a lot of doom-saying about how artificial intelligence (AI) would result in the loss of huge numbers of jobs. However, the picture (across both public and private sectors) is now starting to look not only more nuanced but also more positive. A 2017 report from consultancy PWC suggested that embedding AI across all sectors is likely to create thousands of jobs. In the UK, one estimate suggests that it could contribute as much as 5% of GDP within 10 years. That's not to say that we won't lose jobs, because we undoubtedly will.

Common patent objection #methodpatentclaims executing steps as which are a set of a predefined sequence of steps used to implement an #algorithm, without disclosing any functional limitations pertaining to enablement of features as claimed in form of method steps. In a world where terms and conditions appear everywhere, we can't help but be suspicious about'catches' that exist within particular clauses or how we might set ourselves up for trouble by possibly agreeing to something. Thereby it is imperative to understand legal language as to how to draft patent application which will withstand the objections raised by patent examiner. Patent simply is a kind of IPRs and in AI one important subject where research has gained momentum is Deep reinforcement modules. Scientists globally are working on Deep reinforcement learning.

Though police have been using facial recognition technology for the last two decades to try to identify unknown people in their investigations, the practice of putting the majority of Americans into a perpetual photo lineup has gotten surprisingly little attention from lawmakers and regulators. Lawmakers, civil liberties advocates and police chiefs have debated whether and how to use the technology because of concerns about both privacy and accuracy. But figuring out how to regulate it is tricky. So far, that has meant an all-or-nothing approach. City Councils in Oakland, Portland, San Francisco, Minneapolis and elsewhere have banned police use of the technology, largely because of bias in how it works.

Automated decision-making tools are becoming increasingly ubiquitous in our world. As ML models become more widely adopted, special care and expertise are needed to ensure that artificial intelligence (AI) improves the bottom line fairly. ML models should target and eliminate biases rather than exacerbate discrimination. But in order to build fair AI models, we must first build better methods to identify the root causes of bias in AI. We must understand how a biased AI model learns a biased relationship between its inputs and outputs.

Artificial intelligence (AI) has revolutionized many technology areas. As a few examples, it has already been instrumental in improving and enabling voice recognition algorithms, digital assistants, advertisement recommendation engines and financial trading applications.[1] Significant investment is being made for further development of this promising new technology, with R&D spending on AI predicted to reach $57.6 billion by the end of 2021.[2] Along with these R&D efforts, companies are also trying to protect and monetize their AI inventions, in some cases opting to seek patent protection. From 2002 to 2018, the number of AI patent applications filed with the United States Patent and Trademark Office (USPTO) more than doubled, from 30,000 to 60,000.[3] These R&D efforts are no longer limited to software companies.

Google will change procedures before July for reviewing its scientists' work, according to a town hall recording heard by Reuters, part of an effort to quell internal tumult over the integrity of its artificial intelligence (AI) research. In remarks at a staff meeting last Friday, Google Research executives said they were working to regain trust after the company ousted two prominent women and rejected their work, according to an hour-long recording, the content of which was confirmed by two sources. Teams are already trialing a questionnaire that will assess projects for risk and help scientists navigate reviews, Maggie Johnson, the research unit's chief operating officer, said in the meeting. This initial change would roll out by the end of the second quarter, and the majority of papers would not require extra vetting, she said. Reuters reported in December that Google had introduced a "sensitive topics" review for studies involving dozens of issues, such as China or bias in its services.

As advocates for facial recognition tout the tech's potential to track down the US Capitol rioters, a new Amnesty International campaign has provided a timely reminder of the software's dangers. The NGO has shared a stream of examples of how the software amplifies racist policing and threatens the right to protest -- and called for a global ban on the tech. The Ban the Scan campaign was launched on Tuesday in New York City, where facial recognition has been used 22,000 since 2017. Amnesty notes that the software is often prone to errors. But even when it "works," it can exacerbate discriminatory policing, violate our privacy, and threaten our rights to peaceful assembly and freedom of expression.