What Issues Does AI Ethics Face?
In this section, we cover some of the key issues face in AI ethics:
AI’s impact on jobs
A recent Tech.co survey found that 47% of business leaders are considering AI over new hires, and artificial intelligence has already been linked to a “small but growing” number of layoffs in the US.
Not all jobs are equally at risk, with some roles more likely to be replaced by AI than others. A Goldman Sachs report recently predicted ChatGPT could impact 300 million jobs, and although this is speculative, it’s already been described as a major part of the fourth industrial revolution.
That same report also said that AI has the capacity to actually create more jobs than it displaces, but if it does cause a major shift in employment patterns, what is owed – if anything – to those who lose out?
Do companies have an obligation to spend money and devote resources to reskilling or upskilling their workers so that they aren’t left behind by economic changes?
Non-discrimination principles will have to be tightly enforced in the development of any AI tool used in hiring processes, and if AI is consistently being used for more and more high-stakes business tasks that put jobs, careers and lives at risk, ethical considerations will continue to arise in droves.
AI bias and discrimination
Broadly speaking, AI tools operate by recognizing patterns in huge datasets and then using those patterns to generate responses, complete tasks, or fulfill other functions. This has led to a huge number of cases of AI systems showing bias and discriminating against different groups of people.
By far the easiest example to explain this is facial recognition systems, which have a long history of discriminating against people with darker skin tones. If you build a facial recognition system and exclusively use images of white people to train it, there’s every chance it’s going to be able to be equally capable of recognizing faces out in the real world.
In this way, if the documents, images and other information used to train a given AI model do not accurately represent the people that it’s supposed to serve, then there’s every chance that it could end up discriminating against specific demographics.
Unfortunately, facial recognition systems are not the only place where artificial intelligence has been applied with discriminatory outcomes.
Using AI in hiring processes at Amazon was scrapped in 2018 after it showed a heavy bias against women applying for software development and technical roles.
Multiple studies have shown that predictive policing algorithms used in the United States to allocate police resources are racially biased because their training sets consist of data points extracted from systematically racist policing practices, sculpted by unlawful and discriminatory policy. AI will, unless modified, continue to reflect the prejudice and disparities that persecuted groups already experienced.
There have been problems with AI bias in the context of predicting health outcomes, too – the Framingham Heart study Cardiovascular Score, for instance, was very accurate for Caucasians, but worked poorly for African-Americans, Harvard notes.
An interesting recent case of AI bias found that an artificial intelligence tool used in social media content moderation – designed to pick up “raciness” in photos – was much more likely to ascribe this property to pictures of women than it was to men.
AI and responsibility
Envisage a world where fully-autonomous self-driving cars are developed are used by everyone. Statistically, they’re much, much safer than human-driven vehicles, crashing less and causing fewer deaths and injuries. This would be a self-evident, net good for society.
However, when two human-driven cars are involved in a vehicle collision, collecting witness reports and reviewing CCTV footage often clarifies who the culprit is. Even if it doesn’t, though, it’s going to be one of the two individuals. The case can be investigated, the verdict is reached, justice can be delivered and the case closed.
If someone is killed or injured by an AI-powered system, it’s not immediately obvious about who is ultimately liable.
Is the person who designed the algorithm powering the car responsible, or can the algorithm itself be held accountable? Is it the individual being transported by the autonomous vehicle, for not being on watch? Is it the government, for allowing these vehicles onto the road? Or, is it the company that built the car and integrated the AI technology – and if so, would it be the engineering department, the CEO, or the majority shareholder?
If we decide it’s the AI system/algorithm, how do we hold it liable? Will victims’ families feel like justice is served if the AI is simply shut down, or just improved? It would be difficult to expect family members of the bereaved to accept that AI is a force for good, that they’re just unfortunate, and that no one will be held responsible for their loved one’s death.
We’re still some way off universal or even widespread autonomous transport – Mckinsey predicts just 17% of new passenger cars will have some (Level 3 or above) autonomous driving capabilities by 2035. Fully autonomous cars which require no driver oversight are still quite far away, let alone a completely autonomous private transport system.
When you have non-human actors (i.e. artificial intelligence) carrying out jobs and consequential tasks devoid of human intention, it’s hard to map on traditional understandings of responsibility, liability, accountability, blame, and punishment.
Along with transport, the problem of responsibility will also intimately impact healthcare organizations using AI during diagnoses.
AI and privacy
Privacy campaign group Privacy International highlights a number of privacy issues that have arisen due to the development of artificial intelligence.
One is the re-identification. “Personal data is routinely (pseudo-) anonymized within datasets, AI can be employed to de-anonymize this data,” the group says.
Another issue is that without AI, people already struggle to fully fathom the extent to which data about their lives is collected, through a variety of different devices.
With the rise of artificial intelligence, this mass collection of data is only going to get worse. The more integrated AI becomes with our existing technology, the more data it’s going to be able to collect, under the guise of better function.
Secretly gathered data aside, the volume of data that users are freely inputting into AI chatbots is a concern in itself. One study recently suggests that around 11% of data workers are pasting into ChatGPT is confidential – and there’s very little public information about precisely how this is all being stored.
As the general use AI tools develop, we’re likely to encounter even more privacy-related AI issues. Right now, ChatGPT won’t let you ask a question about an individual. But if general use AI tools continue to gain access to increasingly large sets of live data from the internet, they could be used for a whole host of invasive actions that ruin people’s lives.
This may happen sooner than we think, too – Google recently updated its privacy policy, reserving the right to scrape anything you post on the internet to train its AI tools, along with its Bard inputs.
AI and intellectual property
This is a relatively lower-stakes ethical issue compared to some of the others discussed, but one worth considering nonetheless. Often, there is little oversight over the huge sets of data that are used to train AI tools – especially those trained on information freely available on the internet.
ChatGPT has already started a huge debate about copyright. OpenAI did not ask permission to use anyone’s work to train the family of LLMs that power it.
Legal battles have already started. Comedian Sarah Silverman is reportedly suing OpenAI – as well as Meta – arguing that her copyright had been infringed during the training of AI systems.
As this is a novel type of case, there’s little legal precedent – but legal experts argue that OpenAI will likely argue that using her work constitutes “fair use”.
There may also be an argument that ChatGPT isn’t “copying” or plagiarizing – rather, it’s “learning”. In the same way, Silverman wouldn’t win a case against an amateur comedian for simply watching her shows and then improving their comedy skills based on that, arguably, she may struggle with this one too.
Managing the environmental impact of AI
Another facet of AI ethics that is currently on the peripheries of the discussion is the environmental impact of artificial intelligence systems.
Much like bitcoin mining, training an artificial intelligence model requires a vast amount of computational power, and this in turn requires a massive amounts of energy.
Building an AI tool like ChatGPT – never mind maintaining it – is so resource-intensive that only big tech companies and startups they’re willing to bankroll have had the ability to do so.
Data centers, which are required to store the information needed to create large language models (as well as other large tech projects and services), require huge amounts of electricity to run. They are projected to consume up to 4% of the world’s electricity by 2030.
According to a University of Massachusetts study from several years ago, building a single AI language model “can emit more than 626,000 pounds of carbon dioxide equivalent” – which is nearly five times the lifetime emissions of a US car.
However, Rachana Vishwanathula, a technical architect at IBM, estimated in May 2023 that the carbon footprint for simply “running and maintaining” ChatGPT is roughly 6782.4 tones – which the EPA says is equivalent to the greenhouse gas emissions produced by 1,369 gasoline-powered cars over a year.
As these language models get more complex, they’re going to require more computing power. Is it moral to continue to develop a general intelligence if the computing power required will continually pollute the environment – even if it has other benefits?
Will AI become dangerously intelligent?
This ethical worry was brought to the surface in 2023 by Elon Musk, who launched an AI startup to avoid a “terminator future” through a “maximally curious”, “pro-humanity” artificial intelligence system.
This sort of idea – often referred to as “artificial general intelligence” (AGI) – has captured the imaginations of many dystopian sci-fi writers over the past few decades, as has the idea of technological singularity.
A lot of tech experts think we’re just five or six years away from some sort of system that could be defined as “AGI”. Other experts say there’s a 50/50 chance we’ll reach this milestone by 2050.
John Tasioulas questions whether this view of how AI may develop is linked to the distancing of ethics from the center of AI development and the pervasiveness of technological determinism.
The terrifying idea of some sort of super-being that is initially designed to fulfill a purpose, but reasons that it would be easiest to fulfill by simply wiping humanity off the face of the earth, is in part sculpted by how we think about AI: endlessly intelligent, but oddly emotionless, and incapable of human ethical understanding.
The more inclined we are to put ethics at the center of our AI development, the more likely that an eventual artificial general intelligence will recognize, perhaps to a greater extent than many current world leaders, what is deeply wrong with the destruction of human life.
But questions still abound. If it’s a question of moral programming, who gets to decide on the moral code, and what sort of principles should it include? How will it deal with the moral dilemmas that have generated thousands of years of human discussion, with still no resolution? What if we program an AI to be moral, but it changes its mind? These questions will have to be considered.
