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With advanced research happening in the realm of artificial intelligence (AI), the technology is poised to become smarter than its human creators. But until that day, it is like to harbour sexist, racist and even homophobic tendencies all inherited from its makers social and cultural biases.

This was discussed at some length last year at Rising, one of the countrys biggest gatherings of women trailblazers in the fields of data science and AI. Held on March 8 to commemorate Womens Day, the one-day event hosted more than 250 participants and featured more than 15 sessions led by industry leaders, mostly women.

One of the speakers on the occasion, Director of Citi Saraswathi Ramachandra, provoked a discussion around a hotly debated topic Is AI sexist. According to her, this cannot be firmly answered in the affirmative since AI models can only respond to what it has learned. This means that the real culprit is essentially the training dataset we feed it, and not the technology by itself.

At the heart of it, AI enables tasks to be automated without dependence on step-by-step assistance by humans. How does this work? If a computer is fed enough examples relevant to a given task, it can use ML algorithms to draw an inference. It then finds a way to automatically optimise this approach and goes on to essentially, teaches itself.

To sum it up, AI software trains itself using data that is manually delivered by humans. This means that as it stands now, some level of subjectivity in the outcome cannot be avoided. What is more, as this technology develops, it continues to subtly imbibe these biases from sources like articles and webpages. Thus, our prejudices rub off on technology, thereby reinforcing and exaggerating common stereotypes.

Ramachandra illustrates this with an example.

With chatbots becoming popular across websites and social networks, Microsoft launched its Twitter chatbot Tay in March 2016. However, it was taken down within 24 hours. Tay was designed to mimic a millennial and engage in human conversations with its users. Built on the principles of AI, it was programmed to learn from these interactions and get better at it.

But cheeky Twitter users targeted her vulnerabilities and manipulated her into making deeply sexist and racist statements.

Specifically taking one conversation, Ramachandra spoke about the degree of bias that had been absorbed in the chatbot in less than a day after its launch. The comment by a user went like this We must secure the existence of our people and future for white children. To this, Tay responded by saying that it couldnt agree more. I wish there were more people talking about these things.

According to her, this example clearly demonstrates how quickly machines amplify any biases we may have. This shows that our prejudices play a big role in shaping AI as we know it, potentially becoming even more dangerous as these seep into programs and algorithms.

We often mentally categorise certain jobs on the basis of gender. While homemakers and nurses are more oriented towards women, we have internalised that engineers and doctors are the mainstays of men. This can only be explained away by our innate biases.

This does not portend well for AI systems, that are essentially inheriting these biases and amplifying it with self-learning techniques. So if the fundamental problem rests in the biased datasets we are feeding AI systems, what are the corrective measures that we can take?

A good place to start would be to increase the participation of women in STEM so that they can take up jobs for programming these very AI systems. This becomes imperative because if careful changes are not made to technologies that augment misperceptions about women and marginalised races, AI will continue to proliferate these stereotypes.

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Is artificial intelligence Sexist? The answer is Yes And No - Analytics India Magazine

The White Houses recent efforts to chart a national artificial intelligence (AI) policy are welcome and, frankly, overdue. Funding for AI research and updating agency IT systems is a good start. So is guidance for agencies as they begin to regulate industry use of AI. But theres a glaring gap: The White House has been silent about the rules that apply when agencies use AI to perform critical governance tasks.

This matters because, of all the ways AI is transforming our world, some of the most worrying come at the intersection of AI and the awesome power of the state. AI drives the facial recognition police use to surveil citizens. It enables the autonomous weapons changing warfare. And it powers the tools judges use to make life-changing bail, sentencing and parole decisions. Concerns about each have fueled debate and, as to facial recognition in particular, new laws banning use.

Sitting just beyond the headlines, however, is a little-known fact: AI use already is pervasive in government. Prohibition for most uses is not an option, or at least not a wise one. Needed instead is a frank conversation about how to give the government the resources it needs to develop high-quality and fairly deployed AI tools and build sensible accountability mechanisms around their use.

We know because we led a team of lawyers and computer scientists at Stanford and New York universities to advise federal agencies on how to develop and oversee their new algorithmic toolkit.

Our research shows that AI use spans government. By our estimates, half of major federal agencies have experimented with AI. Among the 160 AI uses we found, some such as facial recognition are fueling public outcries. But many others fly under the radar. The Securities and Exchange Commission (SEC) uses AI to flag insider trading; the Centers for Medicare and Medicaid Services uses it to ferret out health care fraud. The Social Security Administration is piloting AI tools to help decide who gets disability benefits, and the Patent and Trademark Office to decide who gets patent protection.

Still other agencies are developing AI tools to communicate with the public, by sifting millions of consumer complaints or using chatbots to field questions from welfare beneficiaries, asylum seekers and taxpayers.

Our research also highlights AIs potential to make government work better and at lower cost. AI tools that help administrative judges spot errors in draft decisions can shrink backlogs that leave some veterans waiting years (sometimes, close to a decade) for benefits. AI can help ensure that the decision to launch a potentially ruinous enforcement action does not reflect the mistakes, biases, or whims of human prosecutors. And AI can help make more precise judgments about which drugs threaten public health.

But the picture is not all rosy.

First, the government has a long way to go. Our teams computer scientists found that few agency AI uses rival the sophistication found in the private sector, making it harder to realize accuracy and efficiency gains. Some may wish to keep agencies low-tech to limit surveillance or otherwise hamstring government. Its not that simple: Government use of makeshift and insecure AI systems puts everyone at risk. Disabled persons, veterans and all of us deserve better.

Second, AI poses deep accountability challenges. When public officials make decisions affecting rights, the law generally requires an explanation. This reason-giving requirement is deeply embedded in law and even enshrined in the Constitution. Yet sophisticated AI tools are opaque; they do not serve up explanations with their outputs. A crucial challenge is how to subject these tools to meaningful accountability and ensure fidelity to longstanding commitments to transparency, reason-giving and non-discrimination.

To address concerns, agencies could be required to politically ventilate AI tools the way they must new regulations. Or they could be made to benchmark AI tools, reserving a pool of cases for human decision and comparing results to AI-assisted ones. However, there are no one-size-fits-all solutions. Open-sourcing computer code might make sense when agencies distribute welfare benefits. But disclosing details when tax enforcers use AI to identify cheaters will just aid evasion.

Third, if we want agencies to make responsible use of AI, their capacity must come from within. Our research shows that many of the best-designed AI tools were created by innovative, public-spirited agency technologists not profit-driven private contractors. The AI tools that help adjudicate disability benefits at the Social Security Administration came from agency insiders with intimate knowledge of governing law and how administrative judges work.

This makes sense. Government work is often complex. Recruiting skilled technologists and updating outmoded computing systems is crucial to building high-quality AI tools and administering them fairly. But it wont be cheap.

Last, AI can fuel political anxieties. Government AI use creates a risk of gaming by better-heeled groups with resources and knowhow. The SECs algorithmic predictions may fall more heavily on smaller companies that, unlike big Wall Street players, lack a stable of quants who can reverse-engineer the model and keep out of the agencys cross-hairs. If citizens come to believe AI systems are rigged, political support for a more effective, tech-savvy government will evaporate.

In short, this is a pivotal moment for government. Managed well, agency AI use can make the government more efficient, accurate and fair. Managed poorly, AI can widen the public-private technology gap, make agencies more vulnerable and less transparent, and heighten concerns about government arbitrariness and biases that are coursing through American politics.

Wherever the nation lands on facial recognition, government AI use is here to stay. The question now is which of these two visions becomes reality.

David F. Engstrom and Daniel E. Ho are professors of law at Stanford University. Catherine M. Sharkey is a professor of law at New York University. Mariano-Florentino Cullar is a justice on the California Supreme Court and professor of law at Stanford University.

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What to do about artificially intelligent government | TheHill - The Hill

The muse of 21st century art is hidden in lines of code

The mystique of robots taking over humanity, or the notion that humans will eventually be forced to fight for their relevance among super-human robots that outgrow the need of their human creators, is a trope that has existed in artistic expression for decades. The ever-increasing discussion about artificial intelligence has fostered a sleeker, more modern incorporation of technology in art as a subject, a tool and a means of measuring the value of art.

Grimes began her latest project in November 2018 with the release of We Appreciate Power, a nod to the capabilities that lie in endless lines of code and an embrace of the reign of AI. Its futuristic synth-pop with the lyrics, Baby, plug in, upload your mind / Come on, youre not even alive / If youre not backed up on a drive. This statement begins to sound more realistic as the recombinant power of innovation expands.

Artists such as Bjrk have even gone as far as giving AI some creative freedom with her work. She and Microsoft recently partnered to create Krsafn, meaning choral archives in Icelandic, which uses AI to recombine fragments of her music to react to patterns in the weather. For example, the chords sound different during sunrise and sunset. The project takes place inside the hotel Sister City in New York City. Its a generative lobby score powered by Microsoft AI, according to Microsofts website.

Technology has historically had a large influence on music and has helped expand the array of sounds that can be incorporated into a song. There may be some who say that technology has worsened the quality of music, but overall it contributes to musics evolution. This reminds me of the song Intro on Odeszas Summers Gone, with the lyrics, You combine segments of magnetic tape/By these means and many others you can create sounds which no one has ever heard before.

British artist Matthew Stone designed the album cover of FKA Twigs Mary Magdalene by creating digital brushstrokes that resemble paint on canvas, creating a truly three-dimensional shape thats arguably more believable than traditional painting. A computer-generated program always draws a perfect line, but will art created by AI be objectively better?

The incorporation of technology and AI into art are redefining who and what can be an artist. In the case of Krsafn, AI is doing the work for itself and isnt created with human direction. The program is given input and recombines them based on musical rules. Its one thing for AI and tech to be the subject of an artists work, but its another thing entirely when it doesnt need a human artist.

Artists experiences and struggles, whether documented on canvas or with musical chords, hold a value unmatched by data collected to create something that is most likely to be liked by the masses. Good art is disinterested in what people already want and is often a catalyst that breaks the mold a trait on which humans still have a monopoly. Life imitates art wouldnt be very interesting anymore if predicted by a program.

Written by: Josh Madrid arts@theaggie.org

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Art, artificial intelligence and technology - The Aggie

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Over the past year, the Digital Banking Report has conducted several research studies on the deployment and potential impact of data and artificial intelligence on the banking industry. We have found that the improved use of data and advanced analytics can improve customer experiences, generate better marketing results, streamline deposit and lending operations, increase consumer engagement, support innovation, and be a foundation for digital transformation.

Being a data-driven financial institution is no longer optional (if it ever was). In every industry, winners will be determined by how well data and AI can be used for the benefit of the consumer. Big tech firms such as Google, Apple, Facebook and Amazon (GAFA) are setting the pace, delivering experiences that are improving valuations and providing the foundation for entry into financial services. Fintech firms and non-traditional banking challengers are using data and insights to steal business from legacy banks and credit unions.

From tracking social media engagement to looking at spending patterns and the use of existing financial services, a data-driven approach completely changes organic growth opportunities from cross-selling to providing proactive advice. Instead of being a privacy threat, the intelligent use of data can provide a value proposition that the consumer appreciates and may even pay for (similar to how people pay Amazon for the right to shop digitally).

Unfortunately, while there is virtually no question of the benefits of data and AI for the benefit of the consumer, the vast majority of deployment by legacy organizations still focuses on cost reduction and productivity and/or risk management. While these use cases certainly help financial institutions meet quarterly financial goals and protect against losses from fraud, the consumer rarely feels any personal benefit.

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From a consumers perspective, most use of AI for a better experience has been superficial at best. While the industry continues to say that the use of Data and AI is a major trend and is a priority as shown in this years Retail Banking Trends and Predictions report, research on use of AI shows that deployment for the benefit of the consumer has lagged the hype by a significant amount.

Except for the largest financial institutions, and some of the smallest, few organizations profess to be adept at advanced targeting, multichannel communications, real-time contextual offers or proactive advice. This is very disappointing given the marketplace realities across industries.

It is clear that banks and credit unions are testing the use of data and AI across businesses, but they are definitely not as bullish or proficient as public announcements would suggest. Where there is an investment in advanced analytics, our research shows that the impact continues to be focused on back-office efficiency, risk avoidance and cost reductions.

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Not all AI implementations have been internally-focused. For instance, Bank of Americas AI-powered digital assistant, Erica, has more than ten million users and completed 100 million client requests in the first 18 months since introduction. According to Bank of America, The app can be configured to a persons preferences and usage, giving everyone a different home page similar to the way Amazon and Netflix give every user a different home screen.

While not near the final potential of the app, the ability to be notified of a potential overdraft, remind a customer of a recurring payment, understand a customers spending and saving habits and warn a customer about a duplicate payment is a capability that few banks or credit unions can match.

With hundreds of billions of tweets, likes and searches each day, financial institutions have the ability to supplement internal balance and transaction insights to create value for the customer or member. That said, few financial institutions even use the massive data at their disposal internally.

The key is to support intelligent interactions based on this data in real time. The ability to create these type of engagements has become easier and easier with the creation of new technologies and ways to process data. The cost to do this type of analysis has dropped, even though the availability of talent to create and manage models has become more challenging.

Financial institutions can also create digital-driven products that have AI as part of the foundation. This can be done in-house or in collaboration with fintech or big tech providers using open banking APIs and the cloud. As discussed in the Innovation in Retail Banking report published by the Digital Banking Report (and available for free download), this type of collaboration speeds up the innovation process and supports digital transformation.

There is no arguing that organizations must respect the consumers desire for security and privacy and that any use of data for internal and challenges can not be considered roadblocks. Financial institutions of all sizes are beginning to focus on how data and insights can benefit consumers directly, because these same consumers are expecting more from their financial institution partner. Banks and credit unions must use current data and insights to:

Using data and analytics to improve the customer experience is not a new concept. In fact, the banking industry has discussed this capability for decades. The difference today is that the consumer understands the potential of using their data for their personalized benefit. Its time that the banking industry walk the walk as opposed to simply talking the talk.

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Artificial Intelligence in Banking: More Hype Than Reality - The Financial Brand