An area of focus for chatbots is avoiding and reducing conflict during their human-AI interactions. Whether making a product inquiry or trying to resolve a customer service issue, most of us have interacted with AI or chatbots. Anyone who uses Siri or Alexa is likely to have tales of queries gone wrong or frustrated attempts at seemingly simple requests. But in dismissing these as simplistic machines, what we may not appreciate is that they are often sophisticated tools. Their complex programing is designed not only to solve a wide range of queries but also to emulate more complicated interpersonal skills, such as minimizing conflict, essential in many customer service applications.

Click to learn more about author Anne Hardy. Artificial intelligence (AI) is no longer the future – it's already in our homes, cars, and pockets. As technology expands its role in our lives, an important question has emerged: What level of trust can – and should – we place in these AI systems? Trust is the very question the European Union (EU) Commission has set out to answer under its newly proposed EU Artificial Intelligence Act. Margrethe Vestager, Executive Vice President of the European Commission for A Europe Fit for the Digital Age, stated that trust is a must with AI.

This paper surveys the area of Trust Metrics related to security for autonomous robotic systems. As the robotics industry undergoes a transformation from programmed, task oriented, systems to Artificial Intelligence-enabled learning, these autonomous systems become vulnerable to several security risks, making a security assessment of these systems of critical importance. Therefore, our focus is on a holistic approach for assessing system trust which requires incorporating system, hardware, software, cognitive robustness, and supplier level trust metrics into a unified model of trust. We set out to determine if there were already trust metrics that defined such a holistic system approach. While there are extensive writings related to various aspects of robotic systems such as, risk management, safety, security assurance and so on, each source only covered subsets of an overall system and did not consistently incorporate the relevant costs in their metrics. This paper attempts to put this prior work into perspective, and to show how it might be extended to develop useful system-level trust metrics for evaluating complex robotic (and other) systems.

What is the contribution that the arts and humanities can make to our engagement with the increasingly pervasive technology of artificial intelligence? My aim in this short article is to sketch some of these potential contributions. Perhaps the most fundamental contribution of the arts and humanities is to make vivid the fact that the development of AI is not a matter of destiny, but instead involves successive waves of highly consequential human choices. It's important to identify the choices, to frame them in the right way, and to raise the question: who gets to make them and how? This is important because AI, and digital technology generally, has become the latest focus of the historicist myth that social evolution is preordained, that our social world is determined by independent variables over which we, as individuals or societies, are able to exert little control.

AI has provided us with the ability to automate tasks, extract information from vast amounts of data, and synthesize media that is nearly indistinguishable from the real thing. However, positive tools can also be used for negative purposes. In particular, cyber adversaries can use AI (such as machine learning) to enhance their attacks and expand their campaigns. Although offensive AI has been discussed in the past, there is a need to analyze and understand the threat in the context of organizations. For example, how does an AI-capable adversary impact the cyber kill chain? Does AI benefit the attacker more than the defender? What are the most significant AI threats facing organizations today and what will be their impact on the future? In this survey, we explore the threat of offensive AI on organizations. First, we present the background and discuss how AI changes the adversary's methods, strategies, goals, and overall attack model. Then, through a literature review, we identify 33 offensive AI capabilities which adversaries can use to enhance their attacks. Finally, through a user study spanning industry and academia, we rank the AI threats and provide insights on the adversaries.

Allison Fine and I have been looking at artificial intelligence, nonprofits, and philanthropy. While this particular research was focused on giving and fundraising, we have looking at topic with a broader lens. As we continue to explore this topic, I'll be posting more regular updates about new developments. I was really excited to discover research on artificial intelligence that intersects with my work on nonprofit workplace wellbeing (The Happy Healthy Nonprofit). The Partnership on AI's "Framework for Promoting Workforce Wellbeing in the AI-Integrated Workplace" provides a framework and practices to guide employers, workers, and other stakeholders towards promoting workforce wellbeing as AI becomes integrated into the workplace.

Phishing is the activity of a site appearing as another, and trying to deceive the user of the site into mistaking the attacker's site as the one the user wants to use. This has caused an infinite number of fraudulent transactions and other criminal activities. Now think what happens if the person that you think you are looking at in an online video, is not the same person at all. It is a digitally rendered copy of the person, however, this time it's not just a still, it's a moving, talking video of the person with features almost indistinguishable from the person that it is supposed to be. Read along to find more on what I'm talking about.

After several high-profile cases, it's understandable that governments would want to start regulating artificial intelligence (AI), and biometric technology in particular. The Clearview AI scandal has shown that people are really'not OK' with the knowledge that companies scraped the internet for private images in order to train a facial recognition AI solution they then turned around and sold to law enforcement agencies. Additionally, a number of cases by civil rights groups have shown that when AI is employed to make decisions about providing credit, rendering a verdict, or simply verifying the identity of a person, minorities are often discriminated against. At the end of April, the EU adopted a proposal for a regulation called the Artificial Intelligence Act (AIA) designed to regulate AI-based solutions. When these new rules go fully into effect, the EU hopes to become a global trendsetter in AI regulation.

Lawyers have been reluctant to engage with artificial intelligence (AI) and other technology partly because law firm partners haven't given junior staff enough time to learn how it can help them, according to a government-backed report. Funded by government agency Innovate UK, the report found widespread agreement among mainly banking and finance specialists from six large commercial firms that technology was increasingly important but that they were in the dark over what worked best. Focusing on the behavioural science questions of what motivates and inhibits lawyers' choices, legal transaction platform Legatics worked closely over two years with partners at Herbert Smith Freehills, DLA Piper and others, along with some 100 lawyers from Pinsent Masons, Osborne Clarke, Reed Smith and Eversheds Sutherland. Key findings were that 95% of all trainees and associates agreed on the importance of implementation and use of new legal tech, an assessment shared by three-quarters of partners. But fewer than four in 10 understood what was available, with a lack of time for learning or training and insufficient incentive to adopt it two main reasons.

Recently, I learned that the failure rate for machine learning projects is still astonishingly high. Studies suggest that between 85-96% of projects never make it to production. These numbers are even more remarkable given the growth of machine learning (ML) and data science in the past five years. For businesses to be successful with ML initiatives, they need a comprehensive understanding of the risks and how to address them. In this post, we attempt to shed light on how to achieve this by moving away from a model-centric view of ML systems towards a data-centric view. Of course, everyone knows that data is the most important component of ML. Nearly every data scientist has heard: "garbage in, garbage out" and "80% of a data scientist's time is spent cleaning data".