With the rapid increase in AI implementation across industries, businesses with high ethical scrutiny are faced with multiple complaints and litigations over the use of personal data and various other biases and discrimination. IBM reports that only 35% of global consumers trust how AI technology is being implemented by organisations, and 77% believe that organisations must be held accountable for their misuse of AI.
Ethical AI and Responsible AI follow parallel paths yet address different causes. Ethical AI is about moral principles and guidelines used in AI systems and algorithms that align with societal values. Responsible AI is the practical application of Ethical AI, addressing bias, transparency, accountability and other concerns.
Ensuring security in banking is crucial in today’s digital world. A single mishap could cause losses to both the organisation and its customers. In 2021, The National Australia Bank (NAB) piloted facial recognition technology (FRT), to allow customers to verify their identity digitally by comparing ID documents with photos or videos of themselves. They evaluated potential harm, bias, and privacy impacts. NAB’s ethical reviews helped build trust, and they integrated AI ethics principles into their processes. The pilot proved successful, enhancing NAB’s data and AI ethics frameworks.
In Recruitment, biased decision-making is one of the biggest complaints against organisations and hiring managers. However, with automation, many recruiters have switched to AI methods. According to a survey done among 7,504 people from the UK, USA and Australia, 30% said they believe that AI removes human unconscious bias in the workplace. Following a previous discrimination lawsuit, Money Bank, UK conducted a data protection impact assessment and implemented an AI tool to enhance transparency, fairness and a bias-free selection of candidates. Despite having GDPR compliance, automated decision-making still showed its downsides which indicates that not every Ethical AI tool can offer the same results.
COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) is an AI tool used in the U.S. to predict the likelihood of a criminal reoffending, scoring from 1 (lowest risk) to 10 (highest risk). It categorises individuals into low, medium, or high risk based on parameters like age, gender, and criminal history. Defendants with higher scores are more likely to be held in prison before trial. However, public news agencies found the system biased against black defendants, often rating them as higher risk compared to white defendants.
Who can forget the Clearview AI controversy? It sparked a lot of debates because they scraped billions of images from social media and other sites without anyone’s permission. This raised huge red flags about privacy and the potential for misuse, especially when it came to how law enforcement and private entities could use this data.
Setting aside the obvious implications of AI, Ethical AI has been proven to bring benefits to both the company and the customers.
When a company formally establishes the responsible use of AI, it conveys a message to its stakeholders - customers, suppliers, employees and shareholders - that the brand values transparency and fairness, which in turn helps build trust and confidence.
Proactively communicating privacy policies, biases, and security helps organisations stay ahead of possible legal risks. Companies with fully scaled Ethical AI frameworks ensure they are compliant with the evolving regulatory landscape. OpenAI has published guidelines for responsible use of its language model, GPT-3, including restrictions on generating harmful or misleading content.
According to a study by Forbes, 41% of senior executives decided to abandon an AI system altogether when ethical implications were found. Competitive advantage means a lot to a brand, and having a solid ethical framework can help a business win. Ethical AI is not just for technology companies that are considered with high ethical scrutiny, but any company that uses technology as middleware or an enabler to serve customers.
The Key principles of Responsible AI are; Fairness and Bias, Accountability, and Transparency. Following these points, businesses can implement Ethical AI models in their organisational frameworks.
An AI system is as good as it’s trained to be. As humans have biases, datasets fed to these AI models also tend to lean on these biases such as race or gender discrimination. The famous Applecard credit limit case is one such AI bias error that differentiated against genders. Hence, it’s important to train algorithms with unbiased data and have a strict framework for model reviews. Companies must also develop a code of ethics that aligns with their responsibility. This will support business expansion and scalability.
In Responsible AI, the decisions made by AI models must be accountable for output. As machines cannot be held accountable, it’s the responsibility of developers who must use methods to oversee the systems. Accountable AI models generally have governance frameworks and policies for development and deployment.
The magic behind the screen of developing AI models is still new to many users who do not understand how it works. That’s why it’s important to keep every user informed of what they’re engaging with, the capabilities of a service model, and what to expect. If any errors or unfair decisions are made by a system, users deserve to know the reason. Accountability and Transparency usually interlink with Explainable AI (XAI) playing the role of an enabler.
Developers and executers of AI projects must always keep the end-user in mind during the design process, giving more attention to Diversity and Inclusion when building and training an AI model. Different stakeholders can be involved in the testing and training stage to understand the path and perspectives. Remember, AI augments are built by humans for humans.
Communicating data usage and privacy policies to the end user is one of the best ways for both the company and its customers to mutually get on the AI wagon. Furthermore, many mass-scale economies are implementing regulations such as the European Union AI Framework that regulates the use of AI within their geopolitical territory. Understanding such international guidelines and following them when establishing AI models will help companies seamlessly enter new global markets and save on customisation costs.
At Insighture, we have busy implementing various projects inside our Research and Development lab and over the years as AI has come into focus, seen the important of clear guidelines. As an ethical technology company, we want our partners and team to engage in responsible practices that benefit society. Below are core ethical principles we follow that lay the groundwork for ethical AI practices descibed earlier. Incorporate these before you get into detailed testing, metrics and the rest.
In an AI-driven world, Ethical and Responsible AI are crucial for avoiding biases and privacy violations. By ensuring fairness, transparency, and accountability, businesses can build trust and gain a competitive edge. These principles not only help to meet regulatory standards but also foster long-term success and stakeholder confidence. Something to think about because AI is not going anywhere.
The next time you sit down with your Chief Innovation Officer, bring up quality maturity as an agenda item. Artificial intelligence (AI) exists with …
READ MORE
AI has been an anticipated technological breakthrough, taking over most manual white-collar jobs today. PwC’s Global AI Study reveals that AI will …
READ MORE