{ Setting the Standard }

BABL has been on the forefront of developing standards and best practices in the field of Responsible AI, including developing an Ethical Algorithm Assessment framework, partnering with the non-profit ForHumanity to define audit standards for AI governance, and advising the DoD on AI and national security.

Our products and services sit at the current frontier of Responsible AI & Ethics Consulting.

Why us?

Proprietary Methodology

We help our clients avoid ethical, reputational, and compliance risks using our bespoke auditing and ethical risk and impact assessment framework.

Professional Research

We consist of professional academics that contribute to cutting-edge research in responsible innovation and machine intelligence.

Extensive Experience

We use proven strategies cultivated through years of experience in the space of organizational ethics consulting and change management.

How we can help you


Responsible AI Governance Gap Analysis

This is a targeted assessment of the client's current Responsible AI practices and governance in relation to current (and emerging) standards, regulations, and best practices.

The practices assessed include AI development processes, documentation, reviews, testing, monitoring, stakeholder engagement, and external communication and transparency.



Algorithm Risk & Impact Assessment

This is a deep assessment of a particular algorithmic sociotechnical system to identify ethical, compliance, safety, liability, and reputational risk. A full report and recommendations for mitigating those risks are provided.


Algorithmic Bias Assessments and Audits

This is an assessment of potential bias in an algorithmic system for internal development purposes, though results can be shared publicly if the client wishes. To accomplish this, we makes use of inputs from our algorithm risk and impact assessment and highly technical tools and expertise, including:

  • Proprietary algorithms for evaluating bias;
  • Carefully curated test datasets for each industry/use-case;
  • A world-class team of experts that are helping define this burgeoning industry

Bias audits are a stringent independent review of potential bias in an algorithmic system that is shared publicly and based on 3rd party frameworks.


Corporate Training in Responsible AI

This involves developing and delivering training and compliance courses in Responsible AI to fit corporate upskilling needs.

Example areas may include conducting technical bias audits or ethical risk analyses based on proprietaty BABL methodologies.

Case Study: Leading AI Vendor


The challenge

Public and regulatory pressure

A leading AI vendor comes under intense public and regulatory scrutiny for potential bias in its core AI product, eroding the trust of clients.

The solution

A Responsible AI Strategy

Through a multi-workstream project, BABL AI helps the client:

  • Conduct a bias assessment of their core machine-learning algorithm;
  • Develop and execute an Ethical Impact Assessment, identifying key ethical risks and governance mechanisms for mitigating those risks;
  • Develop data collection and labeling best practices, with a focus on mitigating potential sources of societal bias in training data;
  • Implement a data quality and model monitoring program, with demonstrated success measured through continuous improvement and reduced bias in their production algorithms.

The results

Regain of trust with public, regulator, and key clients

Through iterative improvement and transparent bias assessment documentation, the vendor is able to build public trust, address regulatory inquiries with good-faith, and retain critical clients.

Need help? Get in touch with us