Designing Automation Architecture under Purview of Ethical AI Model
Published on : Monday 05-12-2022
Ethical AI models should be the foundation for designing and developing automation architecture, feels Dipak Singh.
Ethical artificial intelligence (AI) models are taking centre stage with regard to designing automation architecture and frameworks. Automation frameworks are platforms created through the integration of diverse software and hardware resources, along with other tools and services on the basis of qualified assumption sets.
While AI is fast becoming the mainstay for most applications, driven by machine learning and fast decisions, these systems are not fully error-proof. At times, seemingly harmless or minor errors may lead to unwarranted outcomes, particularly in circumstances involving activities like recruitment, law enforcement, sanctioning loans, justice, and more.
This is where algorithmic or AI bias comes into play. This means repeated machine learning system errors, leading to outcomes that may turn out to be illegal or grossly unfair. Those building AI-based software do not necessarily aim to create applications which are biased. However, there are always chances of them entering automation architecture. Whenever there is improper architecture or an unsuitable model is used, biases end up forcing their way into the proceedings with absolutely avoidable consequences.
What can be done to counter this darker side of artificial intelligence? Several experts feel that ethical AI models should be the foundation for designing and developing automation architecture. This should be built on core philosophies such as accountability, fairness, trust, and transparency. Ethical AI architecture usually comes with data, model, and governance layers.
The Data Layer – ML (machine learning) is intrinsically deployed for finding recurring patterns across data sets. This data is made by human beings or they take the decisions on the data which is to be used. Staying aware of the chances of biased decision-making and also taking measures for combating potential issues are also responsibilities of human beings. There should be a concerted effort towards proper usage, labeling, and collection of data. This will ensure that algorithms are not encouraged to keep repeating and implementing biases or stereotypes that get into recommendations and predictions. ML models sometimes end up shedding their accuracy after being deployed. There will be newer biases or scenarios or completely new contexts altogether. AI architecture should have a mechanism for garnering real-time human feedback while these inputs can be integrated via retraining. Such patterns enable consistent performance tracking.
The Model Layer – For most ML techniques, the connections between output decision-making and input information is vague. Input information goes through various complex evolutions, while the inner mechanisms may be out of the grasp of the data creator as well. This directly contradicts the principles of fairness. For instance, many people have seen their loan applications turned down by algorithms without proper reasons. There is explainable AI as a technique to solve these issues, where models offer reasons and explanations for final conclusions.
Governance Layer – This is the vital layer with all the tools, tech, and also training and education. A central source of ML systems/models and relevant descriptions will automatically enhance reuse while encouraging the maintenance of consistent operational standards throughout the organisation. Audits are enabled through version control for models, decisions, and datasets alike. Companies can even get back to other versions in case any latest model starts showing signs of problematic and sudden behavior. There has to be a mindset change and we need to wake up about AI decisions not being superior to our own (not at least all the time).
Everyone in the ecosystem should rather focus on the suitable ways of leveraging and deploying machine learning and AI, along with their vulnerabilities or limitations. Getting rid of biases is a core principle upheld by ethical AI architecture. This should be the core foundation for designing automation architecture, keeping fairness and transparency sacrosanct.
Dipak Singh, Lead Data Scientist, INT (Indus Net Technologies), is a Machine Learning Researcher, Corporate Trainer and Educator, engaged in analytics and business modelling in various domains such as Banking, Insurance, Retail, and Pharma.
INT (Indus Net Technologies) is a full-stack software engineering solutions company focusing on the banking, insurance, financial services and pharmaceuticals industries. Over the last 25 years, the company has served nearly 500 clients with human-centric and outcome-driven solutions. INT. has a presence in India, UK, USA, Singapore and Canada.
