It’s a big day for democracy in the US and AI has been playing a part. The emergence of AI-driven ‘voting assistants’, has underscored a set of ethical challenges that hold lessons for commercial sectors adopting artificial intelligence. While these voting assistants were designed to enhance voter engagement and simplify the voting process, they have also posed a risk: the potential spread of misinformation and biased influence, particularly in crucial swing states. The ethical dilemmas are relevant for any industry using AI to drive consumer decision-making, make automated recommendations, or analyse sensitive data.
A double-edged sword for voter influence
According to a Forbes article by Diana Spehar, the introduction of AI-powered voting assistants in the 2024 US election cycle has raised concerns about how AI might influence voter perceptions, especially in politically sensitive swing states. The tools can quickly present users with answers to their election-related questions, such as candidate positions and policy summaries. However, their neutrality is questionable, as they generate responses based on their training data, which might not always be impartial or accurate.
For areas beyond the civic, this is a reminder of the risks of using AI-driven recommendation systems. In any commercial application – whether finance, retail, or healthcare – an AI assistant that subtly favours certain choices based on its training data can lead to biased outcomes, unintentional or otherwise. When customers are unaware of the underlying biases, it creates an ethical dilemma: are these AI systems supporting informed decision-making, or are they nudging users in specific directions for business interests?
The threat of misinformation
Spehar’s article highlights that AI voting assistants can unintentionally propagate misinformation, which becomes particularly dangerous in the highly charged atmosphere of an election. These tools, lacking any real understanding of objective ‘truth’, can amplify biases embedded in their training data or simply generate incorrect information. In a high-stakes context like an election, where misinformation could impact voter turnout and public perception, this is a serious concern.
For companies deploying AI in customer service, advertising, or information dissemination, the stakes might not be as politically charged, but the risks are the same. An AI-driven chatbot in retail, for instance, could inadvertently spread incorrect product information or reinforce biased narratives. Organisations need to proactively address these issues by curating high-quality, accurate data sources and implementing rigorous validation checks to mitigate the risk of spreading misinformation.
Understanding and mitigating AI biases
AI systems learn from vast datasets, which can carry historical, cultural, or social biases. In elections, these biases could inadvertently influence how voting assistants describe certain candidates or policies, as Spehar notes, impacting voter perspectives in subtle ways. This unintentional bias demonstrates a crucial point: AI systems reflect the biases of their data sources, and without oversight, they can reinforce these biases in their outputs.
This lesson is particularly relevant for businesses seeking to incorporate AI into decision-making processes, such as recruitment, financial analysis, or consumer behaviour predictions. For example, if an AI recruitment tool is trained on a dataset with historical hiring biases, it might favour certain demographic groups over others. Recognising and correcting for bias is essential, as allowing biases to persist can result in both ethical and legal repercussions. Implementing diverse datasets, conducting regular audits, and adopting algorithmic fairness measures are steps organisations should take to ensure their AI models support equitable outcomes.
The need for clear AI operations
One of the key insights from the use of AI voting assistants is the importance of transparency. Spehar’s article reveals that many voters are completely unaware of how AI systems actually generate responses. When users don’t understand how an AI makes decisions or recommendations, trust becomes an issue. In elections, this lack of transparency can fuel public scepticism, eroding confidence in the technology and, by extension, the election process itself.
For businesses, transparency is just as critical. When deploying AI-driven systems in any customer-facing role, whether as virtual assistants, recommendation engines, or personalised marketing tools, companies should prioritise clarity and explainability. Customers need to know when they are interacting with AI rather than a human and, ideally, should understand the basics of how that AI makes decisions. Emphasising transparency not only meets ethical standards but also strengthens trust – a key factor in building long-term customer relationships.
Who bears the responsibility?
When AI goes wrong, who is responsible? This question is especially pertinent in election contexts, where errors can have significant public impact. As Spehar’s article suggests, if a voting assistant’s bias or misinformation inadvertently influences an election outcome, the question of accountability becomes complex. Should it fall on the developers, the organisations deploying the technology, or the platform itself?
In business, the question of accountability is just as important. If an AI-driven tool leads to biased hiring, unfair lending decisions, or false product recommendations, who should be held accountable? To navigate this, companies need clear accountability frameworks that assign responsibility at each stage of AI development and deployment. Developing a code of ethics for AI, establishing internal oversight committees, and training employees to oversee AI systems can help ensure that accountability is built into the organisation’s AI strategy from the outset.
Regulatory standards and guidelines
The ethical issues surrounding AI voting assistants also point to the pressing need for regulation. As AI applications expand across sectors, regulatory standards could help ensure that ethical principles are consistently upheld. In elections, regulations could enforce transparency, accuracy, and fairness in AI tools, addressing issues like misinformation and bias.
In the commercial world, regulatory oversight is developing globally. Businesses deploying AI should prepare by following best practices proactively, such as aligning with GDPR for data privacy or developing ethical guidelines to govern AI operations. Proactive compliance with ethical standards can protect companies from future regulatory actions, while also demonstrating a commitment to responsible AI use which resonates with customers and stakeholders.
Respecting and protecting user information
Spehar’s piece also touches on the sensitivity of voter data, highlighting another critical ethical area: data privacy. AI voting assistants undoubtedly access vast amounts of personal data to personalise their responses, which, if mishandled, could lead to breaches of privacy or misuse of sensitive information.
In other sectors, data privacy is equally sensitive. Organisations using AI for personalised recommendations, targeted advertising, or user analytics must handle personal data responsibly. This involves obtaining informed consent, protecting data from unauthorised access, and ensuring that user data is not used beyond its intended purpose. Adopting privacy-first approaches not only helps comply with legal requirements but also strengthens customer trust in a world increasingly aware of data privacy issues.
Building ethics into the AI lifecycle
One overarching takeaway from the AI voting assistant experience is that ethical considerations should be part of AI design from the very beginning. As seen with the example of voting assistants, ethical AI is not just about preventing harm; it’s about actively building systems that promote fairness, transparency, and accountability.
For companies, this means embedding ethical practices across the entire AI lifecycle, from data collection and model training to deployment and user interaction. Organisations can set up ethics committees, perform regular audits for fairness and bias, and establish guidelines that align with core company values. By fostering a culture that prioritises ethical AI, businesses can build trust, enhance brand integrity, and ultimately contribute to an ethical AI landscape.
The deployment of AI voting assistants in the US election shines a light on ethical issues surrounding bias, misinformation, transparency, and accountability which are directly applicable to organisations in the private and public sectors sector exploring AI-based solutions. While AI presents immense opportunities, the lessons from elections underscore that its use must be guided by ethical principles and a commitment to accountability.