Creating a Strong Foundation for Responsible AI | Alteryx
By: Melissa Erbes
Tags: Data, Analytics, Data Science, Big Data, AI

Self-service analytics, like Alteryx, include drag-and-drop capabilities, allowing you to deploy geospatial analysis, natural language processing, and predictive analytics into repeatable workflows. This gives your data science teams more time to focus on building and deploying AI and ML models and to address any risks across the lifecycle of these models.

Incorporate Human Judgment and Accountability

One key principle in the responsible AI framework is the concept of keeping humans in the loop, incorporating human judgment and accountability, and informing decisions appropriately. Since the deployment of AI, there has been a significant delineation between “black-box” and “clear-box” AI. While AI and ML can be trained to perform many tasks without humans, these systems can often operate in a black-box fashion, leaving it unclear as to how these machine-based decisions are made. The converse of this black-box approach is a clear-box approach that enables insight into how machine learning makes predictions.

The Alteryx APA Platform is built specifically with the concept of being human-centered, augmenting human capability regardless of one’s technical acumen. In other words, with Alteryx everyone can participate and benefit from a collaborative advanced-analytics environment. Even those who are not proficient in R or Python nor able to write their own models can take advantage of geospatial, predictive, and ML-based analytic capabilities to collaborate, innovate, and solve. Specifically, with the Alteryx Intelligence Suite, an assisted modeling capability provides documented “clear-box” approach to understanding platform a level of insight and confidence into the results that machine learning models are producing.

Maintain Transparency and Testing

Another key factor in deploying responsible AI is the requirement to maintain accountability and transparency for iterations, versions, and changes made to models. The Alteryx APA Platform is built upon enabling the open documentation of workflows and models. This includes understanding the source, quality, and lineage of data, and the certification and reliability of deployed models and the flexibility to share insights across multiple reporting, visualization, and BI platforms. With the Alteryx APA Platform, organizations within the IC have a unified platform to create actionable insights that can be shared and help propel outcomes.

One of the significant promises of AI and ML is the ability to harness and drive insights within unstructured data, from determining express sentiment in a social media post to modeling topics within textual-based information. Within the Alteryx Intelligence Suite,users get the ability to leverage automated building blocks that deliver capabilities for working with semi-structured and unstructured data through OCR recognition, sentiment analysis, and topic modeling. With these elevated capabilities, unstructured data agencies within the IC are going to have better access to actionable insights.

Mitigate Undesired Bias and Ensure Objectivity

Although not specifically stated as a guiding principle for responsible AI deployment, one key element that needs to be considered is time. When analytic teams are slowed down by manual, mundane, and repetitive processes, they can become stressed, overworked, or behind schedule. The Alteryx APA Platform enables your data teams to automate basic data gathering, cleaning, joining, and analysis functions.

The more time spent on prepping data for advanced analytics, AI, or ML, the less time is spent dedicated to the due diligence needed to ensure responsible analysis, including understanding possible inherent bias contained in data or previous analyses.

In a recent blog by Alan Jacobson, chief data and analytics officer at Alteryx, he writes that “When leveraging artificial intelligence and advanced analytic methods, we must be careful that inputs don’t bias the outcomes. In many cases, models are built based on historical data, and if these data include biases, they can propagate into future decision-making. In one now-famous ‘AI fail’ example, a tech company looking to automatically perform initial resume screening built a model based on the characteristics of historically ‘high-achieving’ employees. However, the inputs were flawed: the tech industry is heavily male-dominated, and their high-achieving employees were thus more likely to be male as a percentage.” As Jacobson explains, “Artificial intelligence doesn’t make moral judgements. It is not inherently biased, but historical data and the creators of the model could be.”

With a strong foundation and ethical framework for responsible AI and an Analytic Process Automation capability, the IC — and your organization — will be well-positioned to build the actionable insights needed to accelerate mission outcomes.