In today’s uncertain world, data-driven decision-making processes are the lifeblood of many businesses. Data-driven decision-making produces more beneficial results than gut feeling alone, but many companies still struggle with this reality. It seems all the more surreal and out of reach when you consider the sheer volume – ever increasing – of data generated daily in the world.
If data is the fuel for obtaining this business insight, artificial intelligence (AI) is the engine that powers this process, delivering new solutions and insights previously out of reach. With each organization becoming a sort of data factory, automation is now the only way to quickly transform billions of rows and thousands of columns of data into information. Advances in AI are helping to provide the autonomous insight and generation of advanced predictions needed to quickly guide data-driven decision-making.
However, not all AI projects are created equal, and the results of any AI system can accelerate, impact, or even severely slow down efforts to build trust in AI.
Learn about ethics
AI models are usually trained on historical data. If this data contains biases, the model can propagate them in future decisions. For example, if a company has historically hired more men than women for technology-related roles and feeds historical resume data into an AI designed to review applications, the resulting model may be biased towards against women who apply.
And unfortunately, this hypothetical situation has already taken place. Amazon had to shut down its own AI recruiting algorithm because the model favored candidates who described themselves using words more often found on the resumes of male applicants, leading to an unfortunate bias against female candidates. Data training and testing are integral to success.
While AI can quickly analyze large volumes of disparate data sources to enable domain experts to make decisions, it cannot replace human judgment. Systemic biases can creep into AI, as 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
The success of AI systems critically depends on the quality of training data, the transparency of internal governance processes, and the skill levels of the humans involved in its creation. AI projects must be able to assess, authenticate, and refresh historical data to simulate outcomes that can dynamically adapt to ever-changing business demands.
All data often comes with some form of bias, whether intentionally or unintentionally. Age, race, gender, medical history, financial status, income, location and many other factors can produce biases. Training datasets used for AI systems must be non-discriminatory to ensure the desired outcome.
With the right approach, ethical AI is within reach of all organizations. Data science and AI play a powerful role in gaining an advantage and getting ahead of the competition, but ethical AI provides long-term benefits and a foundational level of understanding accuracy that, subject to leadership and an appropriate strategy will pay off in the short and long term.
Looking Ahead: The Chief Ethics Officer
Strategies need to be grounded in solid foundations when looking to responsibly design and deploy successful AI projects. Just as a house requires a skilled architect to plan, design, and oversee its construction, responsible deployment of AI and analytics requires a professional trained in data science.
A study commissioned by Alteryx on the state of data literacy found that 42% of data worker employees viewed data ethics as “irrelevant” to their role – casting a shadow over future endeavors AI based. With the prevalence of everyday decisions increasing over time, the urgent need for leadership in AI ethics and building an ethical data culture is becoming increasingly evident. It is crucial that any AI ethics strategy incorporates a human connection and data culture that reduces the risk of bias. To succeed, however, proper leadership and strategy are essential. This is where the Chief Ethics Officer comes in.
Data literacy and ethics go hand in hand when it comes to developing and deploying trustworthy AI that can augment and complement human capabilities. By embedding transparent data ethics practices into day-to-day operations across the organization, an ethics officer provides a level of central oversight and structured governance necessary to mitigate risk by ensuring that there is no there is no misuse of data.
Why Data Ethics Depends on Upskilling Everyone
How to save patterns and alleviate concerns about bias? By democratizing data and analysis. By ensuring that a wide range of domain experts are trained in data literacy, companies can ensure that more perspectives, experiences and expertise are directly capable of take a challenge.
Organizations need a leader, but it’s equally important to bring diverse groups into the process so they can bring additional insight to data collection and analysis. Diverse teams can bring unique insights into datasets and use their own domain experience to assess bias and validity before data reaches production.
While the Chief Ethics Officer takes responsibility for spearheading best practices in the ethical use of data, this requires an enterprise-wide approach built on a solid foundation of multiple and diverse perspectives. . Businesses collect vast amounts of data from multiple sources, and human intelligence is key to taking stock of how data is used to train machine learning algorithms in any AI system. By weaving a web of data skills across the organization, employees will not only discover new problems, see new insights, and take their decision-making to new heights, but they will also help avoid pitfalls and ethical issues related to the deployment of AI. The democratization of data is the key to positive change, and data culture is the foundation of this process.
The future of ethical AI is upon us. With an ethics officer tasked with ensuring that humans and ethics are at the center of AI innovation – the organization’s digital mapper – subject matter experts who know the data can build machine learning models and uncover inconsistencies in data that might otherwise go unnoticed by data scientists without direct domain knowledge.