The Biden administration must prioritize and address all the ways that AI and technology can exacerbate racial and other inequities.

Proponents of expanding the use of artificial intelligence (AI) often point to its potential to stimulate economic growth — increased productivity at lower costs, a higher GDP per capita, and job creation have all been touted as possible benefits. The promise of an economic boost via machine learning is understandably seductive, and private and government actors are now regularly using AI in key areas of economic opportunity, including education, housing, employment, and credit, to name just a few. But as AI adoption is cast as a smart economic investment in the future, it’s important to pause and ask: Whose futures and whose wallets are we talking about?

There is ample evidence of the discriminatory harm that AI tools can cause to already marginalized groups. After all, AI is built by humans and deployed in systems and institutions that have been marked by entrenched discrimination — from the criminal legal system, to housing, to the workplace, to our financial systems. Bias is often baked into the outcomes the AI is asked to predict. Likewise, bias is in the data used to train the AI — data that is often discriminatory or unrepresentative for people of color, women, or other marginalized groups — and can rear its head throughout the AI’s design, development, implementation, and use. The tech industry’s lack of representation of people who understand and can work to address the potential harms of these technologies only exacerbates this problem.

There are numerous examples of the harms that AI can have. AI tools have perpetuated housing discrimination, such as in tenant selection and mortgage qualifications, as well as hiring and financial lending discrimination.

For example, AI systems used to evaluate potential tenants rely on court records and other datasets that have their own built-in biases that reflect systemic racism, sexism, and ableism, and are notoriously full of errors. People are regularly denied housing, despite their ability to pay rent, because tenant screening algorithms deem them ineligible or unworthy.

These algorithms use data such as eviction and criminal histories, which reflect long standing racial disparities in housing and the criminal legal system that are discriminatory towards marginalized communities. People of color seeking loans to purchase homes or refinance have been overcharged by millions thanks to AI tools used by lenders. And many employers now use AI-driven tools to interview and screen job seekers, many of which pose enormous risks for discrimination against people with disabilities and other protected groups. Rather than help eliminate discriminatory practices, AI has worsened them — hampering the economic security of marginalized groups that have long dealt with systemic discrimination.

That’s why today, the ACLU, the Leadership Conference on Civil and Human Rights, Upturn, and two dozen partner organizations are calling on the Biden administration to take concrete steps to bring civil rights and equity to the forefront of its AI and technology policies, and to actively work to address the systemic harms of these technologies. Just two weeks ago, many of the same groups also joined together in an in-depth response to a request for information by federal financial agencies on the use of AI, raising many of the same concerns. Many groups have also offered concrete policy recommendations to federal agencies on addressing technology’s role in discrimination in the domains of hiring, housing, and financial services.

Thus far, federal agencies that regulate industries using AI have not taken the steps necessary to ensure that AI systems are accountable to the people they impact or that they comply with civil rights laws. Federal legislative and regulatory efforts have not yet methodically undertaken the task of ensuring our civil rights laws protect vulnerable people from the harms exacerbated by today’s technologies. In fact, while the Biden administration has made an overarching commitment to center racial equity throughout federal policymaking, the administration’s emerging AI and technology priorities have lacked the necessary focus on equity for people of color and others who have been subject to discrimination and bias. The administration to date has overlooked necessary civil rights and civil liberties perspectives as AI and technology policies are being developed, which risks further perpetuating systemic racism and economic inequality.

The bottom line is that the administration and federal agencies must prioritize and address all the ways that AI and technology can exacerbate racial and other inequities and ensure that its policies and enforcement activities lead to more equitable outcomes. Decades of discrimination have left people of color and Black people in particular, women, and other marginalized groups at an economic disadvantage in the U.S. The Biden administration must work to reverse the trends that continue to this day, which must necessarily include an emphasis on how modern digital technologies perpetuate inequity. The economic and racial divide in our country will only deepen if the administration fails to do so.

The post How Artificial Intelligence Can Deepen Racial and Economic Inequities appeared first on Artificial Intelligence.

Technology and artificial intelligence, we’re told, are creating a New Economy, where algorithms and robots do all our work for us, increasing productivity like never before. Go by the evidence, though, and the reality looks far different.

For decades, U.S. productivity grew by about 3 percent a year. After 1970, it slowed to 1.5 percent a year, then 1 percent. Today, that figure stands at 0.5 percent, and is likely to slump further from the shock of the coronavirus pandemic and mass lockdowns. But it isn’t only numbers. There’s a parallel between the evangelism around AI that we see now and a similar phenomenon we witnessed two decades ago.

The dot-com bubble was also fueled by wishful investors using novel metrics to justify ever-higher stock prices. Instead of something as old-fashioned as profits, investors counted a company’s sales, spending and website visitors. Companies responded creatively. Investors want more sales? I’ll sell something to your company and you sell it back to me. No profits for either of us, but higher sales for both of us. Investors want more spending? We’ll order another thousand Aeron chairs. Investors want more website visitors? We’ll give stuff to people who visit our website. No profits, but more traffic.

One measure of traffic was eyeballs, the number of people who visited a page; another was the number of people who stayed for at least three minutes. Even more fanciful was hits, the number of files requested when a webpage was downloaded from a server. Companies simply put dozens of images on a page, and each image loaded from the server counted as a hit.

Now we have the AI bubble, with plenty of hoopla about how computers are taking over the world. The coronavirus has only added to that rhetoric, and we’re seeing plenty of headlines along the lines of: “Five ways AI is helping fight the coronavirus.”

“AI” was the Association of National Advertisers’ Marketing Word of the Year in 2017. To cash in on the buzz, companies are slapping the AI label on mundane algorithms and advertising themselves as wizards in the field when they have barely begun to think about machine learning. Advertise first, build later.

And just like the meaningless metrics of dot-com commerce, we now have fanciful measures of the triumph of AI. In December, Stanford University released the 2019 edition of its AI Index — a 290-page document with dozens of tables and more than 100 charts — which “tracks, collates, distills and visualizes data relating to artificial intelligence.” When the AI Index was launched in 2017, a Stanford news story boasted that it “will provide a comprehensive baseline on the state of artificial intelligence and measure technological progress in the same way the gross domestic product and the S&P 500 index track the U.S. economy and the broader stock market.”

Nope. GDP is a valuable measure of the amount of goods and services produced each quarter. Divided by hours worked, we have a useful measure of productivity. The S&P 500 is a valuable measure of zigs and zags in the market value of the 500 stocks in the index.

The AI Index, though, does not actually track the progress of the field, but rather reports trends related to it, from the growth in the volume of peer-reviewed AI papers — up by 300 percent between 1998 and 2018 — to increases in the number of conference attendees.

But the value of AI is not measured by these metrics any more than the value of the dot-com companies could be measured by eyeballs and hits. It would be more meaningful to assess the impact of AI on productivity in areas where there have been some successes, such as advertising, e-commerce and news. What are the challenges for AI in more complex areas such as accounting, legal, engineering and health care?

That would provide valuable insights for companies, AI startups, universities and policymakers — especially since the so-called success stories are actually shining examples of the limitations of AI at the moment. The Stanford report cites autonomous vehicles, where success has consistently lagged behind hype. Enabling vehicles to interpret and react to the innumerable objects that manned vehicles encounter on roads and highways and in parking lots, and in every type of weather, from glaring sun to falling snow, is far more complicated than identifying patterns in e-commerce or searching news stories. Autonomous vehicles are flawless in the laboratory, flawed on real highways.

It was the same with IBM Watson, once predicted to revolutionize health care, but is now a cautionary tale for those who gush about breakthrough technologies. Watson did great in the artificial world of Jeopardy!, but has overpromised and underdelivered in the real world of health care.

The speed with which firms adopted word processing, spreadsheet and presentation software in the late 1970s and early ’80s helped us foresee the adoption of enterprise software in subsequent years. In the same way, understanding the speed at which AI diffuses in retail, advertising and news will help us understand how soon it can really revolutionize accounting, legal and engineering applications and (eventually) autonomous vehicles and health care. 

AI has so far feasted on low-hanging fruit, like search engines and board games. Now comes the hard part — distinguishing causal relationships from coincidences, making high-level decisions in the face of unfamiliar ambiguity and matching the wisdom and common sense that humans acquire by living in the real world. Until then, artificial intelligence, for all its potential, will have little measurable effect on the economy.

The post AMID ECONOMIC TURBULENCE, DON’T BET ON ARTIFICIAL INTELLIGENCE TO SAVE US appeared first on Artificial Intelligence.