Fifty-four percent of those intending to use AI are looking at its generative aspect, with engaging tools like ChatGPT and a paragraph or page of information from well-worded prompts. I look forward to the functionality it provides.
Artificial intelligence is good at everything except autocorrecting my name. Even though the software knows I’m human and female, it tries to change me from Paola to Paul, or to Koala. Like it or not, AI is here to stay.
AI has been in our lives for longer than we think. The same is true for technology that is suddenly labeled “new.” Most of our ‘new’ technologies have been in development for him over 10 years. And by the time it’s ubiquitous, it’s been done in many places for 20 or 30 years.
If you want to know how long artificial intelligence has been around and in what capacity, ask AI.
Most chatbots point to 1956 as the year AI was founded. Ideas and applications matured during the 1970s and his 1980s. In the 1990s, decades after the birth of the Internet, as the Internet expanded, machine learning took off and AI accelerated. In other words, AI existed before it became ubiquitous.
Artificial intelligence is becoming more and more prevalent in business. As we journey into this new world of apps and algorithms that make decisions and run background and critical services, we will be bringing along many unwanted stowaways.
AI is picking up on our biases. It’s refreshing even the prejudices we called out a while ago and thought were all but eradicated. If business leaders need to know a few things about AI, bias tendencies should be at the top of the list.
If for no other reason, as lawsuit backlash begins to engulf high-profile AI businesses, leaders want to know exactly what AI bias is. The AI tools of these companies contain many of the same prejudices addressed by protection laws. Algorithms that power the tools being monitored are often rewinding calendars and reintroducing discrimination against groups who needed civil rights legislation as victims. how?
Assuming the AI tool does what it’s told (programmed) to do, to say the AI behaves badly is to assume there are some bad lines in the code. Who would do such a thing in the first place? And it seems like an easy fix, and you breathe a sigh of relief. But that’s not how artificial intelligence works.
Remember machine learning? AI thinks. And unless it is extensively trained and exposed, it will develop and apply the worst our society has had to offer. Left unchecked, AI has the potential to revive many of the trends and practices we thought we left behind. And it’s already happening.
Leaders of organizations building or using AI should pay particular attention. That’s everyone.
AI has the ability to detect and eliminate or avoid bias. You need to train this property into your tools. Early programming must be repeatedly layered with exposure to different scenarios, individuals, and datasets. Without intensive training, testing, or revision, AI recognizes patterns and integrates them into useful methods and shortcuts. After all, AI is part of your automation toolbox.
We expect people to simplify their work responsibilities, especially their day-to-day tasks. AI does the same. The bad behavior of bankers who use gender and race in lending decisions is reflected in related AI. Importantly, biases may be more pronounced in human-human encounters and less detectable in human-machine or machine-machine encounters. But the data reveal that behavior.
AI only performs well with training and regular reviews. Once a leader begins to think of her AI tools as fully digital employees, leaving AI tools unchecked is no longer an option or a habit. We regularly inspect industrial and shop floor automation tools. Because AI is invisible, it may possess properties that are invisible and outside our consciousness. Abandoning this view of AI may be the best decision for leaders.
Some companies could have saved time, money and litigation if they had monitored the consequences of all use of AI tools and proactively addressed evidence of bias.
Algorithmic bias creeps in where bias worked in the past or remains today. When we look at AI in jobs, housing, education, healthcare, banking, credit and finance, we see that biased algorithms and AI tools are the bad guys.
It’s easy. Once these tools “know” the normal proportion of individuals playing a particular role within a given dataset, they start making guesses about who should be where. This tool is simplistic and the results can be discriminatory.
A University of California, Berkeley study of discrimination in consumer finance demonstrated that a biased mortgage algorithm causes black and brown borrowers to pay $765 million more annually than white borrowers. Without proactive steps to address these patterned problems, companies could build AI tools that again prey on the most vulnerable.
Delegating tasks to AI tools and leaving them unsupervised can lead to litigation. Proactive modification means retraining. Leaders can adopt policies that detect and mitigate bias in AI tools.
A number of strategies can reduce the risk and potential for bias to take root. Discovery is a multi-group effort.
AI tools should be rigorously and thoroughly tested by different groups. Tools to operate self-driving cars must be able to fully recognize representative pedestrians, including children and individuals with different mobilities and movement patterns. Chat GPT needs to learn that Australians are not automatically bad tenants.
AI knows nothing new. Automate and package our best and worst thoughts. Fortunately, AI is still young and trainable. Industry leaders can solve these problems. Leaders should be excited about the opportunity to help shape how this not-so-new technology treats people in shaping our future.
1. AI bias may not indicate direct or intentional malice on the part of the company, but the company and its affiliates may be embroiled in backlash against the software.
2. Develop and maintain an understanding of AI bias as part of our obligation to protect our organization from lawsuits and reputational damage.
3. Before deciding to use AI tools anywhere in your business, be sure to understand the history of AI tools and be aware of reports of bias.
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