AI: Artificial Intelligence (AI) has the potential to deliver numerous benefits to businesses, including enhanced productivity, precision and customer contentment. Nonetheless, it should be noted that errors can occur if AI systems are not properly utilised. In fact, Amazon discovered that their AI-based recruitment algorithm was prejudiced towards male candidates applying for technical positions. Moreover, AI-powered mortgage financing was found to be less favourable to Latinx and Black borrowers, who were seen as higher-risk, and predictive criminal profiling software was found to exhibit bias toward Black Americans.
Neglecting diversity and fairness when utilising Artificial Intelligence (AI) can jeopardise an organisation in several ways. Hence, it is crucial for business experts to take the required measures to ensure that technology stays impartial and objective. Failing to supervise and regulate the usage of AI may allow the current bias to become deeply rooted within the systems.
While there is no single code of conduct for AI implementation, organisations must endeavour to promote ethical and transparent use of Artificial Intelligence. Fortunately, an overwhelming 80% of CEOs are willing to take steps to strengthen AI accountability. Here are a few recommended guidelines to achieve this.
Formulate a Clear Definition
Before proceeding with the conversation, it is crucial to arrive at a shared understanding of the term ‘ethical AI.’ A concise and precise definition needs to be established. Various organisations have taken a public stance on this matter. For instance, Microsoft has declared their ambition to “promote ethical AI that prioritises people,” and highlighted values such as fairness, reliability, safety, privacy, security, accessibility, openness, transparency and accountability.
In the video below, Rob High, Chief Technology Officer at IBM Watson, has outlined three pivotal components of ethical AI. Parties involved, such as companies, their consumers and other concerned entities must have faith that AI is being utilised ethically. The Cambridge Analytica scandal on Facebook illustrates that any exploitation of data will not be accepted as it threatens public confidence.
Transparency refers to the ability to trace the origin of data and ensure that robots are suitably trained. Giving individuals command over how their data is used is the fundamental tenet of privacy, making transparency all the more crucial.
Foster Knowledge Acquisition
Once a consensus has been reached on the definition of ethical AI, it is vital to disseminate it among key stakeholders including business partners, employees and customers. According to Forbes, “everyone from top to bottom needs to comprehend what AI is, how it can be utilised, and the responsible considerations relevant to it.” Decision-makers and executives can refer to resources like the World Economic Forum’s AI C-Suite Toolkit to explore complex topics such as fostering an AI-friendly culture, and acquiring the relevant knowledge to enable effective AI implementation.
To ensure that any educational initiative is successful, it is vital to appoint a leader from the governance team to devise the curriculum and encourage hands-on learning involvement among employees, while regularly evaluating participants’ understanding of the essential concepts.
Implement a Governance System
Set up a group of AI experts to develop and maintain ethical AI systems. To address prejudice-related concerns within AI, diversity across race, gender, economic and social background should be ensured among the team members. The team should include business owners, consumers and policymakers from diverse fields. Before proceeding with any other measures, a thorough discussion on ethical AI, and related aspects such as data privacy, bias and transparency (the ability to elucidate the process taken by an algorithm in detail) must be initiated.
Further steps involve assessing the potential hazards resulting from a firm’s AI data and developing systems, rules and checks to ensure effective AI monitoring. As mentioned earlier, there are minimal regulations regarding ethical use of AI. Nevertheless, businesses must prioritise compliance with existing regulations. For instance, the standards outlined by the OECD for artificial intelligence detail how organisations can utilise AI to drive social progress. The creation of dependable AI is contingent on international cooperation, one of the critical components.
With the AI industry evolving at breakneck speed, it is crucial for organisations to devise an AI ethics strategy that aligns with their original values of fairness and transparency. To ensure conformance with new regulations, frequent reviews are necessary to ascertain that these objectives are being upheld. Additionally, with AI becoming increasingly widespread, businesses must invest in human capital and remain primed for the consequent workforce transformations.
Consonant with Present Approaches
For any promising business venture, it is vital to either conform to the company’s pre-existing values or modify them to address the underlying question, “What is the reason behind this?” Attributes such as transparency, accessibility and a sense of common responsibility are all desirable traits to be integrated. All these ideals are relevant to the ethical aspect of artificial intelligence. It is high time to assess whether a lack of shared values or an overarching system for valuing work exists within the organisation.
Instead of regarding ethics as an afterthought in product development, it is crucial to incorporate it right from the beginning. Despite this approach being challenging or inconvenient, its benefits are significant. Steps such as investigating data collection, introducing novel controls, configuring approval systems and performing periodic evaluations of procedures and protocols illustrate measures that can be implemented.
Organisations pledged to ethical AI must demonstrate transparency in their methodologies. In accordance with a recent World Economic Forum article, “Companies should be notably explicit when it comes to data utilisation, application and rationale.” There could be occasions for businesses to be more forthcoming about their technological deployment. For instance, they could specify that a chatbot is an automated system rather than posing as a human.
An approach to showcasing transparency would be to seek insights from specialists on the efficacy of your methodologies and protocols. This might entail participation in peer groups, interaction with policy makers and the creation of educational materials such as white papers, blog posts and articles.