SolasAi provides responsible AI software to help organizations test and justify the fairness of their models, aiming to reduce algorithmic discrimination risks.

SolasAi

SolasAI | 1,239 followers on LinkedIn. We wrap ethical compliance in cool data science!
 | We build trust in AI!

SolasAI is software that detects and removes bias &amp; discrimination from a customer&#39;s decisioning models. It works in credit &amp; insurance underwriting, predictive marketing, healthcare, and employment to name a few use cases.

We Build Trust in AI!, Chief Executive Officer at SolasAI - Harnessing cutting-edge AI & deep industry expertise to illuminate & reduce unfairness…

Larry Bradley

Chief Technology and Innovation Officer at SolasAI

Nick Schmidt

Professional Services Leader & Decision Intelligence Expert > Business Improvement | Service Innovation & Development | Analytics & AI Value Delivery

Dimitris Geragas

Senior Advisor and Board Member * Financial Services * Fortune 500 CRO, Divisional CEO, Marketing EVP

Brian Hughes

SolasAI

Are you interested in how to use Gen AI responsibly? If so, our CTO's ( Nick Schmidt ) session at Google #wedevfest on November 16th is right up your alley! As generative #AI becomes a transformative force across industries, it is vital to establish a framework for its responsible use. In this session, we will explore how established principles of responsible machine learning can guide us in addressing the ethical and governance challenges posed by #genai . Drawing from my experience in AI #governance and #fair lending within the banking sector, I will discuss how traditional machine learning strategies—such as #bias mitigation, transparency, and accountability—can be adapted and expanded to tackle the unique risks inherent in generative models. We'll dive into real-world banking use cases, highlighting both the opportunities and the heightened risks posed by generative AI, and explore actionable steps to ensure AI-driven innovation remains ethical and trustworthy. https://lnkd.in/erirbRHn Dimitris Geragas Brian Hughes Susan Bridge Patrick Hall Perry Abdulkadir Anita Pandey David Van Bruwaene Fion Lee-Madan (she/her) Christian Bonilla Peter Weber

We would like to congratulate Tori Shinohara for her well deserved recognition by Chambers and Partners for her work in Financial Services Regulation: Consumer Finance (Enforcement & Regulation)! Great work Tori!

Looking forward to Nick Schmidt 's, Yolanda D. McGill 's, and Carol Evans ' discussion on the responsible use of #ai and #ml in Consumer Financial Services this Thursday at the Conference on Consumer Finance Law . Check them out at 10AM CT. #responsibleai #machinelearning

I'm looking forward to speaking tomorrow at the ASSOCIATION OF LIFE INSURANCE COUNSEL 's annual conference! I'll be joining an esteemed panel to discuss "What Can We Learn About AI Governance, Strategy and Testing from Other Regulated Industries?" I'll be sharing the stage with industry leaders: - Mary Jane Wilson-Bilik of Eversheds Sutherland , - Michael Brick of Microsoft , - Laura Jehl of Willkie Farr & Gallagher LLP - with Matthew J. Gaul moderating the panel. This panel promises to be an insightful exploration of how different regulated industries approach AI governance, strategy, and testing, and what industry participants in the life insurance sector can learn from them. I'm looking forward to a dynamic discussion and the opportunity to share my insights on responsible AI usage, mitigating algorithmic discrimination,, and establishing effective model governance processes. #ALIC2024 #AIGovernance #LifeInsurance #ResponsibleAI #MachineLearning #AIethics

Today, the MoreThanFair community came together once again in Washington, D.C., to discuss President Biden’s AI Executive Order and its untapped potential. #AI can play a vital role in reducing bias and advancing fairness. MTF members are continuing to work toward a future where #credit is fair and accessible to all. More to come! American Fintech Council , Credit Builders Alliance , National Bankers Association , National Community Reinvestment Coalition - NCRC , National Consumer Law Center , Prosperity Now , UnidosUS (@WeAreUnidosUS) , Camino Financial , Cross River , Esusu , LendingClub , Oportun , SolasAI , Stratyfy , Upstart, Zest AI . ➡️ morethanfair.com

MoreThanFair

We're thrilled to share insights on the emerging NIST Generative AI Risk Profile, brought to you by our colleague, Patrick Hall — an esteemed AI authority. Collaborating with Patrick and other leading experts, we provide unparalleled support to diverse industries in mastering model risk management for generative AI technologies. A common challenge our clients articulate is the novelty and magnitude of risks associated with generative AI. They're eager to leverage these advancements but often grapple with understanding and mitigating the inherent risks. Ensuring customer safety and regulatory compliance remains a paramount concern. The evolving NIST guidance under the AI RMF framework is set to offer crucial benchmarks for safely capitalizing on generative AI's potential. Our role is to guide you through these regulatory challenges while maximizing business outcomes. Our team at SolasAI , BLDS LLC , and Global Economics Group possess over 45 years of expertise applying statistics (now known as "data science") to legal and regulatory compliance issues, as well having significant experience in business optimization (i.e., making algorithms work). We also have a specialized focus on the fair and equitable use of algorithms. In our work, we have developed MRM programs for major players in Financial Services and Health Insurance, conducted LLM audits, testified before the Senate on fairness standards, and are at the forefront of setting benchmarks that regulators will rely on as technology progresses. Interested in how we can help your organization navigate these challenges, seize opportunities, and develop a robust risk management program? Please contact message me or one of my colleagues, Susan Bridge , Steven D. , or Darrin Williams . We look forward to working with you! #genAI #mrm #fairai

Congratulations team for achieving our SOC 2 Type 2 successful audit! This will help us help our customers to minimize the amplification of #bias and #unfairness in #algorithms . To help customers gain deep transparency and improve the outcomes of their models, and reduce the risk of regulatory actions or litigation. Continue the great work, team!

If you missed Nick Schmidt 's remarks (and jokes!) at the United States Senate Committee on Banking, Housing, and Urban Affairs hearing on #ai , here are his 4 Key Principles to Consider for AI Regulation: Materiality: This principle advocates for a risk-based approach in governing AI systems. By focusing more stringent regulation on higher-risk AI applications, resources will be allocated more effectively. For example, a company should not spend as much time reviewing a marketing model as they would an underwriting model that enormously impacts both consumers and the business. Adopting such a risk-based approach ensures that systems with the most significant potential impact are carefully monitored and promotes innovation by not overburdening lower-risk initiatives with unnecessary regulatory constraints. As discussed below, SR 11-7 provides a solid foundation for guiding how materiality is assessed in AI regulation. Fairness: The principle of fairness is central to the responsible deployment of AI. Establishing a clear understanding and expectation of fair AI practices is crucial, particularly in applications that significantly impact individuals, such as housing. Regulators should set expectations that bias and discrimination should be identified and mitigated in AI systems. Existing frameworks relating to measuring and mitigating disparate impact, disparate treatment, and proxy discrimination should guide further regulation of AI fairness. Accountability: AI systems must have accountability mechanisms, especially those with high impact. This involves providing individuals affected by AI decisions a right to appeal, ensuring that there is recourse for those who may be adversely impacted. Additionally, entities that deploy AI systems irresponsibly should face appropriate consequences. Transparency: The principle of transparency mandates clear explanations for decisions made by AI systems. This is fundamental to building trust in AI systems. Understanding the 'why' and 'how' behind AI-driven decisions is crucial for public acceptance and confidence in these technologies, and is further crucial to ensure that systems are fair. #solasai SolasAI

I'm honored that tomorrow I will provide testimony about AI in housing before the U.S. Senate Subcommittee on Housing, Transportation, and Community Development. I'm looking forward to explaining how AI is a tool that has the potential to provide great benefits to consumers and businesses, but that it also comes with an equally significant risk due to issues such as algorithmic discrimination. I will talk about how a measured but firm regulatory approach that builds on existing model governance and anti-discrimination rules is necessary. I'm also eager to hear the testimony of Lisa Rice of the National Fair Housing Alliance and Vanessa Perry of The George Washington University . Thank you very much to Senator Tina Smith for inviting me to testify. I'm attaching a link to the stream. I hope you get a chance to watch it. https://lnkd.in/eAQ4BUUz #FairHousing #AI #ResponsibleAI

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Board Member

Chief Operating Officer

Consultant, Algorithmic Fairness and Data Science

Adam Stromme

Senior Data Science Consultant

Matthew Boutte

Data Science Engineer

Nandish Gupta

Engineering

Consultant, Algorithmic Fairness and Data Science at Solas AI

Perry Abdulkadir

Business Development Consultant

Peter Weber

Consultant, Algorithmic Fairness & Data Science

Stephen Molinari

Director of Engineering

Mike Wilkerson

Bernard Siskin

Anita Pandey

Ben Cox

Christian Bonilla

Chief Technology & Innovation Officer

Chief Executive Officer

Great Lakes Machine Learning Companies

Northeastern US Financial Services Companies (Top 10K)

Machine Learning Companies (Top 10K)

Risk Management Companies With Fewer Than 100 Employees (Top 10K)

SolasAI is software that detects and removes bias & discrimination from a customer's decisioning models. It works in credit & insurance underwriting, predictive marketing, healthcare, and employment to name a few use cases.

We use AI to fix AI. We provide trust & transparency into artificial intelligence, machine learning, and standard statistical models.

We have over 45 years experience doing this, so our software is designed by the people that large banks, insurers, and healthcare providers trust to help them identify and lower their risk. 

If you are tired of paying expensive experts who can't seem to agree, and then leave the hard part of fixing the problems to your expensive and over worked data scientists, then SolasAI is for you. 

We follow the latest decisions and signals from courts, regulators, and law makers, as well as the latest and greatest technology trends for AI and fairness as a whole. This is built into SolasAI so you don't have to figure it out yourself.

SolasAI is also flexible enough to adjust to your business without exposing you to unnecessary risks. We help you identify the fairest and best performing alternatives, and then create just the right amount of data and justification for you to communicate with auditors, regulators, or other stakeholders.

We believe in fairness, but we are practical problem solvers. Bad decisions are bad decisions. We help you make the best decisions while expanding your business and uncovering new possibilities through fairness.

Modelers, are you tired of tools trying to replace you or call your 'babies ugly?' Well SolasAI is your co-pilot to help take your great data science and insights and ensure you also achieve amazing fairness. We solve this together using cool data science. We are your partner and not your competition!

Philadelphia

Pennsylvania

United States

North America

FinRegLab Report and Webinar Examine the Policy ...

SolasAI Improves Bias Explainability and Mitigation Library

Upgrad announces advisory board to scale institute in Singapore

FinRegLab Finds Machine Learning Tools Have Potential to Usher in Fairer Credit Decisions

SolasAI and Capco Pioneer New Compliance Solution to Reduce Bias and Discrimination Arising from AI Use in Financial Services

SolasAI Targets NYC Local Law 144 Functionality with Update to Bias Detection & Mitigation Solution

SolasAI Adds Top-Tier Advisors To Bolster Responsible AI Leadership & Model Risk Management

Artificial Intelligence Pioneers Fairly AI and SolasAI Partner to Enhance Risk Mitigation and Combat Discrimination in Artificial Intelligence

SolasAI Appoints Larry Bradley as Chief Executive Officer

Mindshare Announces CEOs Selected to Participate in its 2023 Cohort

Software designed to increase the fairness of AI models by detecting and quantifying algorithmic disparities, illuminating drivers of disparities, and generating alternatives with less disparity.

A checklist provided to ensure enterprises meet regulatory and societal expectations for model fairness, including testing for disparities and identifying opportunities to reduce them.

An offering to demonstrate the SolasAI solutions tailored to a specific team or organization's needs, showcasing how to detect and mitigate bias in AI models.

A service focused on reducing gender and race disparities in credit models, aiming to maintain high predictive quality while minimizing performance loss.

Consultation services leveraging SolasAI's experience with regulatory bodies, government oversight agencies, and legislators to incorporate insights into AI fairness software.

Solutions that provide clarity on the predictive value of models and the drivers of disparities, aiming to make AI decisions more understandable and justifiable.

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