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  • Writer's pictureIndy Sandhu

Responsible AI: Protecting Data & People

A young girl holds hands with a robot which would not happen without trust
Photo by Andy Kelly on Unsplash

What is Responsible Artificial Intelligence (RAI) and why is it important?

Responsible AI refers to the ethical and accountable use of AI technologies, ensuring that systems are designed and used in a way that respects privacy, fairness, transparency, and human rights. It is important to prevent biases, protect user data, and mitigate potential harm caused by AI systems.

The potential risks of AI range from performance issues to security concerns, and it is crucial to address these concerns in order to protect data, people and society. In this blog, we will explore how responsible AI can be implemented through a human-centred design approach that includes fairness, interpretability, privacy, and safety measures. We will also discuss how to implement responsible AI. Join us as we delve into the future of business with responsible AI at its core.

Understanding the Potential Risks

It is simple to see the opportunities offered by this technology and easy to fear missing out on what they could offer your organisation. Identifying and mitigating unintended consequences is crucial to ensure that the downsides don't outweigh the upsides.

Performance is Considerably Different from What we are Used to

One of the advantages of traditional IT systems is predictability; if you put in a set of data and requests, you get the same result every time. AI systems are different, as they work on hugely complex probability based models that predictability is sacrificed for flexibility and adaptability. This leads to risks that what the models are telling you is not actually the right answer. New methods and approaches, such as transparency, explainability, and testability, are required to provide assurances that generative AI models are correct, they are deployed appropriately, and their results are within expected ranges.

Unfair and Unwanted ML Biases Risk Discrimination

AI models are trained on data; basically the more data used for training, the more accurate the model. Building training sets is an ongoing challenge and it is very simple for them to contain the most commonly available samples at the exclusion of the outliers, i.e. they are biased. When this happens, the results generated by the machine learning model will be biased towards the majority and away from minorities, leading to system outcomes that discriminate against minorities.

Security Concerns are Amplified and Under the Microscope

How is my data used? Who has access to my data? These are questions that are rightly, commonly asked. The evolving nature of this technology is only going to increase the frequency with which they are asked. It is also more complex to answer unless you are very strict with how models are trained.

How do you drive the implementation of Responsible AI?

The following sections outline the approaches necessary to ensure a system utilises Responsible AI; these include Human-Centred Design, Governance Practices, Testing approaches and system transparency. All of which need to be included the solution ecosystem to avoid the risks identified above.

Principles: Embracing a Human-Centred Design Approach

A human-centric design approach puts the individual at the core of the solution; starting from the design stage makes this feel natural, it also reduces the implementation cost. Looking at every decision from the point of view of the user, using persona or other similar approaches, steers design, model building, engineering, testing and deployment decisions.

Embracing a human-centred design approach demands ethical principles (e.g. trust & inclusion), transparency and societal values. Organisations must ensure the development and utilisation of models is aligned with these values.

Inclusion: Build Fairness in

To prevent unfair biases, diverse representation in training data from the real world is crucial. Biases can lead to discriminatory treatment and may be unintentionally perpetuated through data. Utilise regular audits and reviews to identify and address biases. With the latest Large Language Models (LLMs) it is not possible to know precisely what data has been used, in this case testing of the solution is increasingly important. Testing needs to include a wide range of use cases that check against biases, take care to also include mainstream scenarios so that positive discrimination is also avoided.

Transparency & Interpretability: Making it Understandable

AI is new and people are increasingly mistrusting of new technology, this is magnified by the black box nature of the models. Hence, it is more important than in traditional solutions that the decisions made are explainable and these explanations are such that they are open to all who use the system. This transparency needs to be built into the final solution; features like explainers, text or graphical representation of decision trees should be open to those who are interested. Care needs to be taken to ensure that these features have minimal impact on system responsiveness. Despite challenges in complex models, involving domain experts and rigorous testing can ensure successful implementation of a white box AI system.

Transparency fosters trust, accountability and is evidence of sound ethics.

Testing & Monitoring: Reviewing System Performance Pre & Post Deployment

The complex nature of these systems requires different and more comprehensive approaches to ensure solutions provide results within expected and acceptable bounds both before and after release to users.

Traditional testing tools will struggle to work with AI solutions; they rely on a known result to be the outcome of a known set of inputs, this is often not now the case. While the future of AI will no no doubt include QA tooling eventually catching up with this problem, in the meantime an automated solution that checks a wide range of use cases for results in a given range or even utilise AI to validate results should be considered. Manual testing should also be used extensively; it is important to keep humans in the loop!

Monitoring production operations is also crucial. Real User Monitoring (RUM) should be used to ensure the production system is providing results in expected ranges should be implemented. Alongside this analytical techniques should be used on the anonymised inputs and outputs of the actual user interactions. This should be used to identify anomalies, emerging patterns and to ensure the models are not drifting as they are fed on ever increasing volumes of data.

Data Collection and Handling: Don't Forget the Basics

Safeguarding sensitive data and implementing robust privacy measures should be exactly the same as in any system development. The vast majority of solutions will still collect data from individuals, data that for a variety of reasons needs to be transported and stored. Minimising the amount of data stored is the single most important approach, but this also needs to be backed up by robust and transparent data protection practices. As with most things in system development, building security and privacy in from the start is the most efficient and cost effective approach.

When it comes to data collection and handling in AI system development, it is crucial to identify whether your ML model can be trained without the use of sensitive data. If it is essential to process sensitive training data, strive to minimise the use of such data.

The added complexity of model deployment and testing should not change the methods used to safeguard data; mishandled data is the easiest way to lose trust, regardless of how good your solution might be.

Institute Governance: Keeping Responsible AI at the Top of the Priority List, Maintaining Trust

Implementing many of the approaches outlined above will require additional effort; in some cases it will be tempting to cut corners to meet deadlines or other business demands. Letting any of these practices slip, even the smallest amount, will compromise the potential value of AI. Hence, it is vital that robust but lightweight governance processes exist and are followed.

Issues can be identified early with the adoption of a governance framework that include:

  • Independent testing of the solution; focusing on biases and transparency

  • Regular reviews of system performance

  • Data Protection Assessments

  • Audits of testing approaches and their effectiveness

Responsible AI at ConnectingYouNow

ConnectingYouNow is a AI powered co-pilot backed by a flexible analytics engine delivers unprecedented customer experience and unrivalled behavioural analytics, delivering the insights organisations need to drive efficiency.

We prioritise ethics and trust throughout our product development and deployment. Our models are utilised with transparency and explainability, so users understand how the system works. We audit for bias and continually work to be inclusive of all people and prevent unintended discrimination.

ConnectingYouNow's models are developed with integrity. We monitor performance across diverse data and user groups to ensure no groups suffer unintended consequences from our recommendations. Our goal is responsible innovation - creating reliable, respectful AI to empower people.

At ConnectingYouNow, we have ensured that the AI we use is developed and governed in the interests of society and the public good. We took great care to create an AI search co-pilot that augments human intelligence in a highly transparent, equitable and accountable way. We do this by conducting user research, engaging the community we serve to get feedback. Additionally, we test vigorously to ensure the language the AI uses is fair and that the user is not asked for any unnecessary information. We continuously test scenarios and add to them to eliminate any biases; ensuring the focus is finding the best match based on the user's needs and not who the user is.

At ConnectingYouNow we started our journey knowing that everything we did had to be responsible and that included our development and use of AI. The best practices described above were derived from the methods we use everyday. They have lead to a product that delivers on the promised benefits of Artificial Intelligence that also ensures all of our users are treated fairly and their data is protected now and in the future.

How Does Responsible AI Influence the Future of Business?

Responsible AI plays a pivotal role in shaping the future of business by prioritising user trust and societal values, driving ethical deployment and governance principles. This fosters inclusive and sustainable practices, ultimately influencing public policy, data governance, and sustainability.

Conclusion

In conclusion, AI is increasingly important to drive a modern organisation but responsible AI is crucial for protecting both data and people and hence organisational reputation. Understanding the potential risks, embracing a human-centred design approach, ensuring fairness and transparency, prioritising privacy and security, and implementing safety measures are all essential steps in promoting ethical and responsible AI practices.

By addressing performance risks, security concerns and societal factors, businesses can harness the power of AI while minimising potential negative impacts. Designing and deploying models for fairness, avoiding biases, and reviewing system performance contribute to inclusivity and trust.

Transparency allows users to understand and trust systems, while privacy and security measures safeguard sensitive information. Identifying potential threats and developing strategies to combat them ensure the safety of AI technologies.

Implementing responsible AI through guidelines and best practices not only protects data and individuals but also influences the future of business. By embracing responsible AI, companies can build trust, foster innovation, and create a positive impact on society.

The future of business is intrinsically tied to the responsible and transparent development of AI models and their utilisation in ever more complex systems.

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