Whether it is flawed demand forecasting or human resource softwares that inadvertently discriminate; model problems common as well as dangerous when not monitored.
Today, nearly any business can be brought to heels by a failed Machine Learning (ML) models. Despite the risks, most businesses are confidently (and correctly) ploughing ahead. Nearly every industry relies on Machine Learning-powered systems to boost profitability productivity, and even save lives. Businesses need to strike a balance between the immense power, and potential risks of Artificial Intelligence, while maximizing positive outcomes for customers and society as a whole.
Here are a few things that every business can do to ensure the sustainability of its AI initiatives as they scale:
The teams deploying AI, and the datasets used to train models must be diverse.
In the world of Artificial Intelligence and Machine Learning, data can occasionally obscure the harsh realities of the real world. As ML models are trained on historical data, they have the potential to amplify any existing discrimination or unequal power structures. AI trained on housing data from the last few years, for example, may reflect the legacy of redlining.
The only long-term solution is diversity – and explicit hiring goals. Enterprises need executive accountability for the success of these efforts and transparency to the board or public.
Enterprises should establish a codified framework for ethical risk management in the context of AI
A siloed technology-centric approach alone will not be sufficient to mitigate all risks or ensure that AI is ethically responsible. The solution must include implementing systems that identify both ethical and organizational risks across the organization. While technology is frequently required to bring to light the correct problems, employees must be empowered to act on these insights. Fortunately, there are numerous resources are available to assist an enterprise in initiating this process.
Also Read: Five Ways to Build Ethics and Trust in AI
Enterprises should ensure a modernized data policy that allows AI practitioners to access protected data when necessary
To achieve a responsible AI framework, it is necessary to modernize data access policies and, in some cases, to expand permissions by role. While the majority of enterprises excel at this in context of software development – where access to production systems is tightly controlled – some have detailed governance in place for access to customer data in machine learning.
Many Machine Learning teams have a history of lacking access to protected class data due to legal liability concerns. This is beginning to change because such data across the entire model lifecycle is critical for ensuring accountability and ensuring the outputs are not biased or discriminatory.
Put an end to blind AI shipping
There is a need for improved tools to monitor and troubleshoot Machine Learning model performance. Teams also need assistance in resolving issues before they negatively impact business results. But today, there exists a rapidly maturing ecosystem to help lean teams more effectively validate and monitor models as they encounter production issues. Specialized platforms can assist businesses in troubleshooting complex systems, explaining black-box, and providing guardrails when making high-stakes decisions.
Internal visibility should be increased, the black box should be opened, and the ROI of AI should be quantified. As Artificial Learning and Machine Learning become more critical, the technical teams deploying Machine Learning models and their executive counterparts must be completely aligned.
As businesses prioritize long-term value creation, sustainable models should play a more significant role in the conversation. By taking a few simple steps, organizations can significantly increase the likelihood that their initiatives will be resilient and beneficial to all stakeholders.