Developers of artificial intelligence (AI), machine learning (ML) and biometric-related technologies have "a moral and ethical duty" to ensure the technologies are only used as a force for good, according to a report written by the UK's former surveillance camera commissioner. Developers must be cognizant of both the social benefits and risks of the AI-based technologies they produce, and have a responsibility to ensure it is used only for the benefit of society, said the whitepaper, which was published by facial-recognition supplier Corsight AI in response to the European Commission's (EC) proposed Artificial Intelligence Act (AIA). "Organisational values and principles must irreversibly commit to only producing technology as a force for good," it said. "The philosophy must surely be that we put the preservation of internationally recognised standards of human rights, our respect for the rule of law, the security of democratic institutions and the safety of citizens at the heart of what we do." It added a'human in the loop' development strategy is key to assuaging any public concerns over the use of AI and related technologies, in particular facial-recognition technology.

Explainability is becoming an important requirement for organizations that make use of automated decision-making due to regulatory initiatives and a shift in public awareness. Various and significantly different algorithmic methods to provide this explainability have been introduced in the field, but the existing literature in the machine learning community has paid little attention to the stakeholder whose needs are rather studied in the human-computer interface community. Therefore, organizations that want or need to provide this explainability are confronted with the selection of an appropriate method for their use case. In this paper, we argue there is a need for a methodology to bridge the gap between stakeholder needs and explanation methods. We present our ongoing work on creating this methodology to help data scientists in the process of providing explainability to stakeholders. In particular, our contributions include documents used to characterize XAI methods and user requirements (shown in Appendix), which our methodology builds upon. Keywords: Explainable Artificial Intelligence · Interpretable Machine Learning · Stakeholder needs · Methodology.

SAN ANTONIO, July 07, 2021 (GLOBE NEWSWIRE) -- CloudCommerce, Inc. (CLWD), a technology driven provider of digital advertising solutions, today announced that it has retained Dr. Peter Holden, a leading artificial intelligence (AI) strategist and IP expert, who will assist the Company with the expansion of AI capabilities to its SWARM platform and development and protection of its intellectual property to protect these innovations. "Great teams require great players and Dr. Peter Holden is a great player in the world of applying Artificial Intelligence (AI) and cognitive technologies to real-world problems," said Andrew Van Noy, CloudCommerce CEO. "We are very fortunate to have Peter advising us as we continue the process of transforming CloudCommerce into a true technology company." Dr. Holden has spent the last 25 years leveraging advanced or'deep' technologies as both an investor as well as hands-on operator having established and/or led three tech funds to date and taken multiple companies through to sale or IPO. His relevant technical background, holding a Ph.D. in A.I. and a Honda Post-Doctoral AI Fellowship award from Tokyo University, has allowed him to help companies embrace new and emerging Machine Learning, Deep Learning and cognitive technologies which enable transformative changes to create a competitive advantage.

An anonymous reader quotes a report from Motherboard: With only 30 minutes of audio, companies can now create a digital clone of your voice and make it say words you never said. Using machine learning, voice AI companies like VocaliD can create synthetic voices from a person's recorded speech -- adopting unique qualities like speaking rhythm, pronunciation of consonants and vowels, and intonation. For tech companies, the ability to generate any sentence with a realistic-sounding human voice is an exciting, cost-saving frontier. But for the voice actors whose recordings form the foundation of text-to-speech (TTS) voices, this technology threatens to disrupt their livelihoods, raising questions about fair compensation and human agency in the age of AI. At the center of this reckoning is voice actress Bev Standing, who is suing TikTok after alleging the company used her voice for its text-to-speech feature without compensation or consent.

In popular fiction, artificial intelligence (AI) often goes rogue. Arthur C Clarke's soft-spoken HAL 9000 in the movie "2001: A Space Odyssey" turns sinister, killing astronauts on a mission to Jupiter. Remember Skynet, the antagonist with genocidal goals from James Cameron's movies "The Terminator" and "Judgment Day"? Fiction now seems to be spilling over into real life. AI has run amok and been benched in multiple business deployments. In one instance, an AI had to be shut off after it spewed outrageously lewd and racist comments within 16 hours of being launched because of data poisoning.

Employers have increasingly embraced artificial intelligence ("AI") in the workplace, using the technology to maximize efficiency in nearly every aspect of the employment relationship including hiring, performance management, and discipline. The use of AI, however, comes with attendant risks. Indeed, while one might assume that AI is an ideal tool to serve as a neutral decision-maker, this technology is not infallible. It has been repeatedly shown that algorithms underpinning these AI products are capable of inheriting conscious and unconscious biases of the people that write the code. These products can also adopt biases embedded in external training data the applications process once they are unleashed.

Recently, Microsoft announced that it was terminating dozens of journalists and editorial workers at its Microsoft News and MSN organizations. Instead, the company said, it will rely on artificial intelligence to curate and edit news and content that is presented on MSN.com, inside Microsoft's Edge browser, and in the company's Microsoft News apps. Explaining the decision, Microsoft issued a statement to the Verge. The statement reads: "Like all companies, we evaluate our business on a regular basis. This can result in increased investment in some places and, from time to time, re-deployment in others. These decisions are not the result of the current pandemic."

At the center of this reckoning is voice actress Bev Standing, who is suing TikTok after alleging the company used her voice for its text-to-speech feature without compensation or consent. This is not the first case like this; voice actress Susan Bennett discovered that audio she recorded for another company was repurposed to be the voice of Siri after Apple launched the feature in 2011. She was paid for the initial recording session but not for being Siri. Rallying behind Standing, voice actors donated to a GoFundMe that has raised nearly $7,000 towards her legal expenses and posted TikTok videos under the #StandingWithBev hashtag warning users about the feature.

Artificial Intelligence (AI) systems are increasingly deployed in the public sector. Existing public procurement processes and standards are in urgent need of innovation to address potential risks and harms to citizens. Read our primer based on our research and on input from leading experts in the public sector, data science, civil society, policy, social science, and the law to learn about pathways forward. The COVID-19 pandemic has underlined how biases can manifest in many different aspects of public use technology. For example, federal COVID-19 funding allocation algorithms have favored high-income communities over low-income communities due to historical biases prevalent in the training data. AI solutions that can be implemented fast are typically provided by private companies. As more and more aspects of public service are infused with AI systems and other technologies provided by private companies, we see a growing network of privately owned infrastructure. As government entities outsource critical technological infrastructure (such as data storage and cloud-based systems for data sharing and analysis) to private companies under the guise of modernizing public services, we see a trend towards losing control over critical infrastructure and decreasing accountability to the public that relies on it.

In late June, the MIT Technology Review reported on the ways that some of the world's largest job search sites--including LinkedIn, Monster, and ZipRecruiter--have attempted to eliminate bias in their artificial intelligence job-interview software. These remedies came after incidents in which A.I. video-interviewing software was found to discriminate against people with disabilities that affect facial expression and exhibit bias against candidates identified as women. When artificial intelligence software produces differential and unequal results for marginalized groups along lines such as race, gender, and socioeconomic status, Silicon Valley rushes to acknowledge the errors, apply technical fixes, and apologize for the differential outcomes. We saw this when Twitter apologized after its image-cropping algorithm was shown to automatically focus on white faces over Black ones and when TikTok expressed contrition for a technical glitch that suppressed the Black Lives Matter hashtag. They claim that these incidents are unintentional moments of unconscious bias or bad training data spilling over into an algorithm--that the bias is a bug, not a feature. But the fact that these incidents continue to occur across products and companies suggests that discrimination against marginalized groups is actually central to the functioning of technology.