Let’s start with the answer: AI itself isn’t inherently ageist but it does reflect and amplify age bias already present in society. AI systems are trained on massive datasets, and if that data reflects existing prejudices, the AI will learn and amplify those biases. Imagine an AI hiring tool trained on resumes where men historically got hired for certain positions more often. The AI might pick up on that and favour male applicants, even if their qualifications are equal. If that data includes age stereotypes, the AI can learn and perpetuate those biases. For example, an AI resume screener trained on data with a bias towards younger workers might unfairly disadvantage older applicants.
Even without biased data, the algorithms themselves can bake in bias. For example, imagine an algorithm that decides who gets discounts at a store. It might be designed to favour people with a long history of shopping there, which could disadvantage younger customers who haven’t had the chance to build up a shopping history. This is like assuming older customers are more loyal simply because they’ve been around longer.
AI can also reinforce stereotypes by consistently making decisions that align with them. Think of an AI system that helps decide who gets promoted at a company. If it keeps promoting people who have been with the company for a long time, it might overlook younger employees who have the skills and talent to move up but just haven’t been there as long. This is similar to how ageism can lead to stereotypes about older workers being set in their ways and less adaptable, even if that’s not true.
Mitigating AI Ageism
There are a few ways to mitigate AI ageism. On data, diversity in data collection ensures the AI is exposed to a wider range of experiences and reduces the chance of bias creeping in. Data auditing regularly assesses the data used to train AI systems for potential biases related to age. Techniques like AI Fairness 360 or IBM Watson OpenScale can help identify these biases. Finally, debiasing techniques like weighting data points or removing outliers that skew results can clean and adjust data sets to minimise age bias.
Another way to avoid ageism and bias in AI is to ensure algorithmic fairness. Developing and using metrics that specifically assess age bias in AI algorithms allows developers to track progress and identify areas needing improvement. Counterfactual fairness testing is another method that involves simulating scenarios where an individual’s age is changed to see if the AI’s output changes unfairly. This helps identify age-related biases in the decision-making process.
One issue with AI is this idea of the black box: not knowing how it works and simply accepting its abilities as helpful and worthwhile. As such, it’s vital that we develop AI systems that can explain their reasoning behind decisions. This transparency allows for human oversight and identification of potential age bias in the algorithm’s logic.
On the user experience, inclusive design, AI education, and user testing with diverse demographics all work towards a better world. Designing AI systems that are accessible and user-friendly for people of all ages and technical abilities might involve offering different interface options or providing clear instructions.
AI education and training might look like providing resources and training to educate people of all ages about AI and how it works. This can help build trust and understanding, reducing apprehension among older adults who might be unfamiliar with the technology. Developing AI interfaces that are simpler, have larger fonts, and offer voice commands can make them more accessible for seniors. Initiatives that teach seniors basic computer skills and how to use online services can empower them to participate in the digital world.
User testing with diverse demographics might include older adults in the testing phase of AI development to ensure their needs are considered. This feedback can help identify and address age-related usability issues. By implementing these strategies, we can work towards developing AI systems that are fair and inclusive for people of all ages.
AI’s Role in Everyday Life
Many older adults might not have Internet access or the devices needed to interact with AI-powered services. This can exclude them from essential services like online banking, healthcare portals, or government resources that are increasingly shifting to digital platforms. Not to mention that using some AI interfaces and applications can require some technical know-how. Seniors who are unfamiliar with technology might struggle to navigate these systems, leaving them feeling frustrated and dependent on others.
Sure, AI assistants can offer some level of companionship, but they cannot replace human interaction. Overdependence on AI for social needs can lead to feelings of isolation and loneliness among seniors. If social activities and communication primarily occur online, seniors who are digitally excluded might miss out on connecting with friends and family, exacerbating existing social divides.
In an AI-driven world, seniors are more vulnerable to financial exploitation. Managing online finances or applying for benefits can be complex, and seniors struggling with digital interfaces might be more susceptible to errors or manipulation by malicious actors. Older adults are often targeted by scams because they might be more trusting or less familiar with how to identify online fraud. AI-powered scams can be particularly sophisticated, making it even harder for seniors to detect them.
There’s also the issue of lost autonomy. AI in healthcare or assisted living facilities can be helpful, but over-reliance can diminish seniors’ sense of independence. For instance, an AI constantly reminding someone to take medication might feel infantilising. There are also privacy issues. As AI becomes more integrated into daily life, seniors might have less control over their personal data. This can raise privacy concerns and make them feel vulnerable.
The ideal scenario might involve AI working alongside humans, providing assistance but not replacing human interaction and support systems for seniors. By acknowledging these challenges and working towards inclusive solutions, we can ensure that AI uplifts rather than diminishes the well-being of our senior population.
