Fraudsters Used AI to Mimic CEO's Voice in Unusual Cybercrime Case The Wall Street Journal Criminals used artificial intelligence-based software to impersonate a chief executive's voice and demand a fraudulent transfer of funds in March in what …

A woman's job application is rejected because of a recruiting algorithm that favors men's résumés. A girl dies by suicide after graphic images of self-harm are pushed up on her feed by social media algorithms. A black teen steals something and gets rated high-risk for committing future crime by an algorithm used in courtroom sentencing, while a white man steals something of similar value and gets rated low-risk. In recent years, advances in computer science have yielded algorithms so powerful that their creators have presented them as tools that can help us make decisions more efficiently and impartially. But the idea that algorithms are unbiased is a fantasy; in fact, they still end up reflecting human biases.

Employed across industries, AI applications unlock smartphones using facial recognition, make driving decisions in autonomous vehicles, recommend entertainment options based on user preferences, assist the process of pharmaceutical development, judge the creditworthiness of potential homebuyers, and screen applicants for job interviews. AI automates, quickens, and improves data processing by finding patterns in the data, adapting to new data, and learning from experience. In theory, AI is objective--but in reality, AI systems are informed by human intelligence, which is of course far from perfect. Humans typically select the data used to train machine learning algorithms and create parameters for the machines to "learn" from new data over time. Even without discriminatory intent, the training data may reflect unconscious or historic bias. For example, if the training data shows that people of a certain gender or race have fulfilled certain criteria in the past, the algorithm may "learn" to select those individuals at the exclusion of others.

It may not yet be clear how societies will guard against the potential downside of artificial intelligence -- including algorithmic bias, invasions of privacy and unjustified profiling -- but it's already abundantly clear that safeguards are needed. That's the bottom line from Wednesday night's panel discussion on AI bias, presented in Seattle by EqualAI and LivePerson. Both of the panel's presenters have a stake in figuring out how to address AI's downsides: LivePerson is interested in how chatbots and other AI-enabled tools can smooth interactions between companies and the customers they serve, while EqualAI is an initiative supported by the likes of Arianna Huffington, Wikipedia's Jimmy Wales and LivePerson CEO Robert Locascio to reduce AI bias. "Companies are creating AI to change the world," said EqualAI executive director Miriam Vogel, who focused on equal-pay issues and bias training for law enforcement during her time at the Obama White House and the Justice Department. "They're trying to do good, they're trying to reach people who have not been reached, start conversations that haven't happened otherwise -- knowing that [implicit bias] is not necessarily coming from a malicious act. It's coming from human actions," she said.

Criminals used artificial intelligence-based software to impersonate a chief executive's voice and demand a fraudulent transfer of €220,000 ($243,000) in March in what cybercrime experts described as an unusual case of artificial intelligence being used in hacking. The CEO of a U.K.-based energy firm thought he was speaking on the phone with his boss, the chief executive of the firm's German parent company, who asked him to send the funds to a Hungarian supplier. The caller said the request was urgent, directing the executive...

CEOs often live by the numbers--profit, earnings before interest and taxes, shareholder returns. These data often serve as hard evidence of CEO success or failure, but they're certainly not the only measures. Among the softer, but equally important, success factors: making sound decisions that not only lead to the creation of value but also "do no harm." While artificial intelligence (AI) is quickly becoming a new tool in the CEO tool belt to drive revenues and profitability, it has also become clear that deploying AI requires careful management to prevent unintentional but significant damage, not only to brand reputation but, more important, to workers, individuals, and society as a whole. Legions of businesses, governments, and nonprofits are starting to cash in on the value AI can deliver.

In her new book Race After Technology: Abolitionist Tools for the New Jim Code, Ruha Benjamin breaks down the "New Jim Code," technology design that promises a utopian future but serves racial hierarchies and racial bias. When people change how they speak or act in order to conform to dominant norms, we call it "code-switching." And, like other types of codes, the practice of code-switching is power-laden. Justine Cassell, a professor at Carnegie Mellon's Human-Computer Interaction Institute, creates educational programs for children and found that avatars using African American Vernacular English lead Black children "to achieve better results in teaching scientific concepts than when the computer spoke in standard English." But when it came to tutoring the children for class presentations, she explained that, "We wanted it [the avatar] to practice with them in'proper English.' Standard American English is still the code of power, so we needed to develop an agent that would train them in code-switching."

For all their differences, big tech companies agree on where we're heading: into a future dominated by smart machines. Google, Amazon, Facebook, and Apple all say that every aspect of our lives will soon be transformed by artificial intelligence and machine learning, through innovations such as self-driving cars and facial recognition. Yet the people whose work underpins that vision don't much resemble the society their inventions are supposed to transform. WIRED worked with Montreal startup Element AI to estimate the diversity of leading machine learning researchers, and found that only 12 percent were women. That estimate came from tallying the numbers of men and women who had contributed work at three top machine learning conferences in 2017.

It has been called the'dark heart' of artificial intelligence (AI) – the complicated'black box' of hidden machine learning algorithms that many would have us believe will allow AI to take our jobs and run our lives. But before that can happen AI must be integrated into our everyday systems and protocols – including regulation. Product users and stakeholders must also have trust in AI and machine learning – otherwise they simply won't use it. New interpretability techniques are now making it possible to lift the lid on the black box. Overcoming the "Why should I trust you?" scepticism about AI and machine learning is perhaps the biggest challenge that businesses need to master to gain trust from their stakeholders – customers, employees, shareholders, regulators and broader society.

Facial recognition technology is spreading fast. Already widespread in China, software that identifies people by comparing images of their faces against a database of records is now being adopted across much of the rest of the world. It's common among police forces but has also been used at airports, railway stations and shopping centers. The rapid growth of this technology has triggered a much-needed debate. Activists, politicians, academics and even police forces are expressing serious concerns over the impact facial recognition could have on a political culture based on rights and democracy. As someone who researches the future of human rights, I share these concerns.