Many sci-fi movies have painted artificial intelligence (AI) to be an omen of humanity's demise. The first cinematic appearance of AI was in a 1927 silent German film, "Metropolis." Throughout the entire film, a humanlike robot had the sole intention of wreaking havoc throughout the city. Despite this initial negative depiction of AI, technology has been racing toward the development of advanced artificial intelligence. According to Forbes, "AI-focused companies raised $12 billion in 2017 alone."
When working with their clients Accenture under Tricarico's guidance focuses on "on guiding (their) clients to more safely scale their use of AI, and build a culture of confidence within their organizations." Not all companies have an established north star of AI use. Companies and partners like Accenture are vital to these companies and their proper and ethical use of the technology.
Many industries are utilizing AI. However, in this paper, we look at its applications in the aerospace, fintech, autonomous vehicles, and health care industries, where better AI hardware, software, solutions, and services are creating many opportunities. Data integrity, privacy policies, decision system guidelines, and holistic regulations are continuously evolving in these industries. This ecosystem is now ripe for service providers and system integrators to play their parts, with AI adoption achieving appreciable return on investment. Key applications of AI in this space include optimizing operational efficiencies, assuring robustness of systems, data and image interpretation, and human augmented decision-making. Other applications include automation of processes and workflows, better compliance, improved performance, and reliability platforms, unmanned derivative systems (in finance) and digital and virtual assistants. Figure 1 summarizes AI's importance across the four industries discussed in this paper.1-36 The primary drivers of AI are data privacy, security, cost, risk, authenticity, guarantee and improved decision systems. Each driver has its own specific impact and relevance from a business adoption and operations perspective. The driver ensures that applications will have business significance and are attuned to regulations, while having close association with global and geography-specific ecosystems. Also, the drivers ensure quicker adoption to enhance operational efficiency, without compromising on the end-user experience. Regulatory and government bodies play a vital role in assessing and formulating guidelines for adopting AI in the business value chain.
In September last year, Google's cloud unit looked into using artificial intelligence to help a financial firm decide whom to lend money to. It turned down the client's idea after weeks of internal discussions, deeming the project too ethically dicey because the AI technology could perpetuate biases like those around race and gender. Since early last year, Google has also blocked new AI features analysing emotions, fearing cultural insensitivity, while Microsoft restricted software mimicking voices and IBM rejected a client request for an advanced facial-recognition system. All these technologies were curbed by panels of executives or other leaders, according to interviews with AI ethics chiefs at the three US technology giants. Reuters reported for the first time their vetoes and the deliberations that led to them reflect a nascent industry-wide drive to balance the pursuit of lucrative AI systems with a greater consideration of social responsibility.
Results released June 16, 2021 – Pew Research Center and Elon University's Imagining the Internet Center asked experts where they thought efforts aimed at ethical artificial intelligence design would stand in the year 2030. Some 602 technology innovators, developers, business and policy leaders, researchers and activists responded to this specific question. The Question – Regarding the application of AI Ethics by 2030: In recent years, there have been scores of convenings and even more papers generated proposing ethical frameworks for the application of artificial intelligence (AI). They cover a host of issues including transparency, justice and fairness, privacy, freedom and human autonomy, beneficence and non-maleficence, freedom, trust, sustainability and dignity. Our questions here seek your predictions about the possibilities for such efforts. By 2030, will most of the AI systems being used by organizations of all sorts employ ethical principles focused primarily on the public ...
Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology's impact on society. We chat with Lofred Madzou about AI as a journey to understand ourselves through smart machines, scepticism about wholesale job loss, understanding that "you are not your data", dissecting the European proposal for AI regulation, examples of types of AI activities under regulation, the spirit of the regulation – human rights-centric, risk-based approaches, infringement exposition and compliance… Lofred Madzou is a Project Lead for AI at the World Economic Forum, where he oversees global and multistakeholder AI policy projects. He is also a research associate at the Oxford Internet Institute where he investigates various methods to audit AI systems. Before joining the Forum, he was a policy officer at the French Digital Council, where he advised the French Government on technology policy. Most notably, he has co-written chapter five of the French AI National Strategy, entitled "What Ethics for AI?".
We have entered a new era of machine learning (ML), where the most accurate algorithm with superior predictive power may not even be deployable, unless it is admissible under the regulatory constraints. This has led to great interest in developing fair, transparent and trustworthy ML methods. The purpose of this article is to introduce a new information-theoretic learning framework (admissible machine learning) and algorithmic risk-management tools (InfoGram, L-features, ALFA-testing) that can guide an analyst to redesign off-the-shelf ML methods to be regulatory compliant, while maintaining good prediction accuracy. We have illustrated our approach using several real-data examples from financial sectors, biomedical research, marketing campaigns, and the criminal justice system.
In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human society. The intention of developing AI is to benefit humans, by reducing human labor, bringing everyday convenience to human lives, and promoting social good. However, recent research and AI applications show that AI can cause unintentional harm to humans, such as making unreliable decisions in safety-critical scenarios or undermining fairness by inadvertently discriminating against one group. Thus, trustworthy AI has attracted immense attention recently, which requires careful consideration to avoid the adverse effects that AI may bring to humans, so that humans can fully trust and live in harmony with AI technologies. Recent years have witnessed a tremendous amount of research on trustworthy AI. In this survey, we present a comprehensive survey of trustworthy AI from a computational perspective, to help readers understand the latest technologies for achieving trustworthy AI. Trustworthy AI is a large and complex area, involving various dimensions. In this work, we focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems. We also discuss the accordant and conflicting interactions among different dimensions and discuss potential aspects for trustworthy AI to investigate in the future.
April was a watershed moment for AI regulation. The European Union (EU) Commission published its AI Act, an ambitious proposal for a comprehensive legislative framework for AI - the first from a major global economy. In the words of Margrethe Vestager, EU Commission Executive Vice President and Technology Commissioner, the aim is'for Europe to become a global leader in trustworthy AI'. The Act will apply to organisations providing or using AI systems in the EU. But it will also apply to providers and users located in other countries, including the UK, if their AI systems affect individuals in the EU.
We review practical challenges in building and deploying ethical AI at the scale of contemporary industrial and societal uses. Apart from the purely technical concerns that are the usual focus of academic research, the operational challenges of inconsistent regulatory pressures, conflicting business goals, data quality issues, development processes, systems integration practices, and the scale of deployment all conspire to create new ethical risks. Such ethical concerns arising from these practical considerations are not adequately addressed by existing research results. We argue that a holistic consideration of ethics in the development and deployment of AI systems is necessary for building ethical AI in practice, and exhort researchers to consider the full operational contexts of AI systems when assessing ethical risks.