President Biden President Biden is meeting with the President with an AI expert to discuss the risks of AI. Sam Altman and Elon Musk have been publicly declaring their concern. Consulting giant Accenture was the latest firm to put its money into AI and announced its plans to spend three billion dollars on AI technology and to increase its AI-focused workforce to 80,000. It’s in addition to other consulting firms, with Microsoft, Alphabet, and Nvidia joining the race.
Large companies don’t wait for the bias issue to be solved before adopting AI. That’s why it’s much more important to address one of the most difficult issues that face all the major artificial intelligence (AI) models. AI models. However, AI regulations will require time.
Because each AI model is developed by humans and based on human-generated data, It is impossible to eliminate bias. However, developers should try to limit the amount of “real-world” bias they replicate in their models.
The bias of real-world events in AI
To comprehend the impact of real-world biases, Imagine the scenario of an AI model that is trained to identify who is eligible to get a mortgage. Training that model based upon the individual decisions of human loan officials — including some who may implicitly and unintentionally not lend to people who belong to certain religions, races, or gender identities — carries the risk of replicating their biases from the real world in the output.
AI gives us a unique opportunity to standardize the services in a manner free of bias. However, if we fail to minimize the biases in our models can lead to standardizing extremely flawed services for the benefit of a subset and to the detriment of other users.
Here are three steps developers and founders should take to ensure that they get it right:
Choose the best way to train your AI model
ChatGPT is a good example. It is part of the larger classification of machine learning as it is a large-language model (LLM), meaning it ingests huge quantities of text information and infers connections between words within the text. On the other hand, for the user, this translates to the LLM filling in the blanks with the most likely statistical word based on the context in response to a question.
There are a variety of methods of training data to be used in model-based learning. Some models for health technology use, for instance, big data because they create their AI using patient records or the individual decisions of doctors. For those who create models that are specialized to industry, for instance, HR or medical AI, these big-data strategies could lead in a way that is more biased than is necessary.
Imagine the scenario of an AI chatbot that is trained to interact with patients and produce medical summaries of their presentations to doctors. If created using the methodology described earlier, it would create its output using the information -in this instance, records of millions of patients.
The model could deliver accurate results at high speeds but incorporates the biases of millions of patient records. In this sense, big-data AI models are a symbiosis of biases that are difficult to identify and fix.
Another alternative to these machine-learning strategies, particularly for industries-specific AI, can be to build your machine using the most reliable source of information within your field to ensure that bias doesn’t transfer. In the field of medicine, that means peer-reviewed medical research. In law, it might be the legal documents of your state or country, and, in the case of autonomous cars, it could be the actual traffic rules instead of data from the individual drivers.
These texts were written by humans and had a bias. But if you consider that every doctor tries to learn the best medical literature and all lawyers spend endless hours reading legal documents, these documents can be an ideal basis for developing a less biased AI.
- Balance literature is constantly changing with real-world information.
There’s a lot of human bias in my medical field. Still, it’s also true that various races, ethnicities and socio-economic classes, geographical locations, and genders have different risk levels for certain illnesses. A greater number of African Americans suffer from hypertension than Caucasians do, while Ashkenazi Jews seem to be famously more prone to certain diseases than other groups.
Some differences are worth noticing in providing the highest quality treatment for patients. However, it’s essential to comprehend the underlying causes of these variations from the literature before incorporating these into your model. Do doctors prescribe women particular medications at a higher frequency due to a bias towards women and putting women at a higher risk of developing some specific disease?
If you can identify the root of the cause, you’ll be better able to address it. Let’s revisit the mortgage instance. Fannie Mae and Freddie Mac, which back many mortgages in the U.S., found those of color most likely to make a living from gig-economy employment, Business Insider reported last year. They could not disproportionately prevent the people from getting mortgages as these incomes are viewed as unstable, even though many gig-economy workers have solid rent histories.
To overcome this biasedness, Fannie Mae decided to include the rent-payment history factor in its credit evaluation decision-making. Founders need to develop models that are flexible that can balance legitimate, evidence-based literature from industry with real-world realities that are changing the real world.
Include transparency in your AI model
To identify and correct for bias, you’ll require a glimpse into how your model comes to its conclusions. Many AI models need to trace their roots back to the source of their origins or provide a reason for their results.
These models typically deliver responses with astonishing precision, as evidenced by ChatGPT’s fantastic success. However, if they fail, it’s nearly impossible to pinpoint what went wrong and how to avoid bias or inaccurate output from happening again.
