In today’s uncertain world, many businesses have pinpointed data-driven decision-making as the fuel behind their very survival.

While data-driven decision making produces continuously more beneficial results than gut feeling alone, many businesses still struggle to make data-led decisions a reality, a goal that could be pushed further out of reach by the sheer – ever increasing – volume of data generated around the globe daily.

If data is the fuel needed to drive those business insights, then artificial intelligence (AI) is the engine that powers this process – delivering new solutions and insights previously out of reach.

With every organisation essentially becoming a data factory, automation is now the only way to refine the billions of rows and thousands of columns of data swiftly into insights.

Advances in AI are helping provide the autonomous insight and advanced prediction generation needed to guide data-driven decision-making at speed. But not all AI projects are equal, and the outcomes of any AI system can either accelerate, impact, or severely stall efforts to build AI trust.

Training for Ethics

AI models are typically trained on historical data. If these data contain biases, then the model can propagate bias into future decision-making.

For example, if a company has historically hired more men than women for tech-related roles, and feeds historical CV data into an AI designed to review job applications, the resultant model may be biased against women applying for these roles.

This is not a hypothetical. E-commerce giant Amazon had to shelve its own AI recruiting algorithm, as the model favoured candidates who described themselves using words more commonly found on male applicants’ CVs, leading to an unfortunate bias against female applicants.

Data training and testing are integral to success. While AI can quickly analyse large volumes of disparate data sources to empower domain experts to make decisions, AI cannot replace human judgment.

Systemic data bias can creep into AI because historical data may not be fully representative, and minority groups may not be present in these data resources at all.

The Value of Data Ethics

Successful AI systems are primarily dependent on the quality of training data, the transparency of the internal governance processes, and the skill levels of the humans involved in its creation.

AI projects need to be able to assess, authenticate and discount historical data to simulate outcomes that can dynamically adjust to ever-changing business requirements.

All data often comes with some form of bias, whether intentionally or not. Age, race, gender, health history, financial standing, income, location, and more can produce bias. Training data sets used for AI systems must be free of discrimination to ensure the desired output.

Given the right approach, ethical AI is within the grasp of every organisation. Data science and AI play a powerful role in gaining an edge and getting ahead of the competition, but ethical AI provides long term benefits and a foundational level of insight accuracy that will – given the right leadership and strategy – pay off both the short and long term.

Looking to the Future: The Chief Ethics Officer

Strategies need to be built upon a strong foundation when looking to responsibly design and deploy successful AI projects. Just like a house requires a trained architect to plan, design and oversee its construction, the responsible deployment of AI and analytics requires a professional trained in data science.

Alteryx commissioned research into the state of data literacy in the UK and found, shockingly, that 42% of employees responsible for data work saw data ethics as “irrelevant” to their role – casting a shadow over future AI-based projects.

As the prevalence of daily decisions scales up over time, the pressing need for leadership in AI ethics, and the delivery of an ethical data culture, becomes ever more apparent.

It is crucial that any AI ethics strategy has a human connection baked in and a data culture that reduces bias risks. To succeed, however, the right leadership and strategy is essential. This is where the Chief Ethics Officer comes in.

Data literacy and ethics go hand in hand when developing and deploying trustworthy AI capable of augmenting and complimenting human capabilities.

By embedding transparent data ethics practices within the everyday business of the whole organisation, a Chief Ethics Officer provides a level of central oversight and structured governance needed to mitigate risk by ensuring there is no misuse of data.

Why Data Ethics is Dependent on Upskilling for All

How do you safeguard models and alleviate any concerns around bias and prejudice? Through the democratisation of data and analytics.

By ensuring a diverse range of domain experts are trained in data literacy, businesses can ensure that a wider array of viewpoints, experiences, and expertise are directly able to meet a challenge.

While organisations need a leader of the effort, it is equally important to bring diverse groups into the process so they can provide more insight into the data gathering and analysis.

Diverse teams can provide unique insights into datasets and can utilise their own domain experience to assess bias and validity before data reaches the production stage.

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Although the Chief Ethics Officer shoulders the responsibility for spearheading best practices for the ethical use of data, the journey requires a company-wide approach built upon a strong foundation of multiple, diverse viewpoints.

Businesses collect vast amounts of data from multiple sources, and human intelligence is critical to taking stock of how data is used to train ML algorithms within any AI system.

By weaving a fabric of data literacy across the organisation, employees will not only discover new problems, see new insights and take their decision-making to new heights, but also help to avoid the pitfalls and ethical issues around deploying AI. Data democratisation is key to driving positive change, and data literacy underpins this process.

The future of ethical AI stands at our doorstep. With a Chief Ethics Officer responsible for ensuring humans and ethics are at the centre of AI innovation – the organisation’s digital cartographer – data-literate subject-matter experts can build machine-learning models and discover data inconsistencies that might otherwise go unnoticed by those data scientists lacking direct domain knowledge.

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