AI Orthopraxy: Walking the Talk of Trustworthy AI

In today’s digital age, trust has become a precious commodity. It’s the invisible currency that fuels our interactions with technology and brands. Building trust, especially in technology, is a costly and time-consuming process. However, the payoff is immense. When users trust a system or a brand, they are more likely to engage with it, advocate for it, and remain loyal even when faced with alternatives.

One of the most effective ways to build trust in technology is to ensure it aligns with societal goals and values. When a system or technology operates in a way that benefits society and adheres to its values, it is more likely to be trusted and accepted.

However, artificial intelligence (AI) has faced significant challenges. Despite its immense potential and numerous benefits, trust in AI has suffered. This is due to various factors, including concerns about privacy, transparency, potential biases, and the lack of a clear ethical framework guiding its use.

This is where the concept of AI Orthopraxy comes in. AI Orthopraxy is all about the correct practice of AI. It’s about ensuring that AI is developed and used in a way that is ethical, responsible, and aligned with societal values. It’s about walking the talk of trustworthy AI.

In this talk, I will discuss the concept of AI Orthopraxy, the recent developments in AI, the associated risks, and the tools and strategies we can use to ensure the responsible use of AI. The goal is not just to highlight the challenges but also to provide a roadmap for moving forward in a way that is beneficial for all stakeholders.

Large Language Models (LLMs) and Large Multimodal Models: The Ethical Implications

The journey of Large Language Models (LLMs) has been remarkable. From the early successes of models like GPT and BERT, we have seen a rapid evolution in the capabilities of these models. The most recent iterations, such as ChatGPT, have demonstrated an impressive ability to generate human-like text, opening up many applications in areas like customer service, content creation, and more.

Parallel to this, the field of vision models has also seen significant advancements. Introducing models like Vision Transformer (ViT) has revolutionized how we process and understand visual data, leading to breakthroughs in medical imaging, autonomous driving, and more.

However, as with any powerful technology, these models come with their own challenges. One of the most concerning is their fragility, especially when faced with adversarial attacks. These attacks, which involve subtly modifying input data to mislead the model, have exposed the vulnerabilities of these models and raised questions about their reliability.

As someone deeply involved in this space, I see both the immense potential of these models and the serious risks they pose. But I firmly believe these risks can be mitigated with careful engineering and regulation.

Careful engineering involves developing robust models resistant to adversarial attacks and biases. It involves ensuring transparency in how these models work and making them interpretable so that their decisions can be understood and scrutinized.

On the other hand, regulation involves setting up rules and standards that guide the development and use of these models. It involves ensuring that these models are used responsibly and ethically and that there are mechanisms in place to hold those who misuse them accountable.

AI Ethics Standards: The Need for a Common Framework

Standards play a crucial role in ensuring technology’s responsible and ethical use. In the context of AI, they can help make systems fair, accountable, and transparent. They provide a common framework that guides the development and use of AI, ensuring that it aligns with societal values and goals.

One of the key initiatives in this space is the P70XX series of standards developed by the IEEE. These standards address various ethical considerations in system and software engineering and provide guidelines for embedding ethics into the design process.

Similarly, the International Organization for Standardization (ISO) has been working on standards related to AI. These standards cover various aspects of AI, including its terminology, trustworthiness, and use in specific sectors like healthcare and transportation.

The National Institute of Standards and Technology (NIST) has led efforts to develop a framework for AI standards in the United States. This framework aims to support the development and use of trustworthy AI systems and to promote innovation and public confidence in these systems.

The potential of these standards goes beyond just guiding the development and use of AI. There is a growing discussion about the possibility of these standards becoming recommended legal practice. This would mean that adherence to these standards would not just be a matter of ethical responsibility but also a legal requirement.

This possibility underscores the importance of these standards and their role in ensuring the responsible and ethical use of AI. However, standards alone are not enough. They need to be complemented by best practices in AI.

AI Best Practices: From Theory to Practice

As we navigate the complex landscape of AI ethics, best practices serve as our compass. They provide practical guidance on how to implement the principles of ethical AI in real-world systems.

One such best practice is the use of model cards for AI models. Model cards are like nutrition labels for AI models. They provide essential information about a model, including its purpose, performance, and potential biases. By providing this information, model cards help users understand what a model does, how well it does, and any limitations it might have.

Similarly, data sheets for datasets provide essential information about the datasets used to train AI models. They include details about the data collection process, the characteristics of the data, and any potential biases in the data. This helps users understand the strengths and weaknesses of the dataset and the models trained on it.

A newer practice is the use of Data Statements for Natural Language Processing, proposed to mitigate system bias and enable better science in NLP technologies. Data Statements are intended to address scientific and ethical issues arising from using data from specific populations in developing technology for other populations. They are designed to help alleviate exclusion and bias in language technology, lead to better precision in claims about how NLP research can generalize, and ultimately lead to language technology that respects its users’ preferred linguistic style and does not misrepresent them to others.

However, these best practices are only effective if a trained workforce understands them and can implement them in their work. This underscores the importance of education and training in AI ethics. It’s not enough to develop ethical AI systems; we must cultivate a workforce that can uphold these ethical standards in their work. Initiatives like the CSEAI promote responsible AI and develop a workforce equipped to navigate AI’s ethical challenges.

The Role of the CSEAI in Promoting Responsible AI

The Center for Standards and Ethics in AI (CSEAI) is pivotal in promoting responsible AI. Our mission at CSEAI is to provide applicable, actionable standard practices in trustworthy AI. We believe the path to responsible AI lies in the intersection of robust technical standards and ethical solid guidelines.

One of the critical areas of our work is developing these standards. We work closely with researchers, practitioners, and policymakers to develop standards that are technically sound and ethically grounded. These standards provide a common framework that guides the development and use of AI, ensuring that it aligns with societal values and goals.

In addition to developing standards, we also focus on state-of-the-art collaborative AI research and workforce development. We believe that responsible AI requires a workforce that is not just technically competent but also ethically aware. To this end, we offer training programs and resources that help individuals understand the ethical implications of AI, upcoming regulations, and the importance of bare minimum practices like Model Cards, Datasheets for Datasets, and Data Statements.

As the field of AI continues to evolve, so does the landscape of regulation, standardization, and best practices. At CSEAI, we are committed to staying ahead of these changes. We continuously update our value propositions and training programs to reflect the latest developments in the field and to ensure our standards and practices align with emerging regulations.

As the CSEAI initiative moves forward, we aim to ensure that AI is developed and used in a way that is beneficial for all stakeholders. We believe that with the right standards and practices, we can harness the power of AI in a way that is responsible, ethical, and aligned with societal values in a manner that is profitable for our industry partners and safe, robust, and trustworthy for all users.

Conclusion: The Future of Trustworthy AI

As we look toward the future of AI, we find ourselves amidst a cacophony of voices. As my colleagues put it, on one hand, we have the “AI Safety” group, which often stokes fear by highlighting existential risks from AI, potentially distracting from immediate concerns while simultaneously pushing for rapid AI development. On the other hand, we have the “AI Ethics” group, which tends to focus on the faults and dangers of AI at every turn, creating a brand of criticism hype and advocating for extreme caution in AI use.

However, most of us in the AI community operate in the quiet middle ground. We recognize the immense benefits that AI can bring to sectors like healthcare, education, and vision, among others. At the same time, we are acutely aware of the severe risks and harms that AI can pose. But we firmly believe that, like with electricity, cars, planes, and other transformative technologies, these risks can be minimized with careful engineering and regulation.

Consider the analogy of seatbelts in cars. Initially, many people resisted their use. We felt safe enough, with our mothers instinctively extending an arm in front of us during sudden stops. But when a serious accident occurred, the importance of seatbelts became painfully clear. AI regulation can be seen in a similar light. There may be resistance initially, but with proper safeguards in place, we can ensure that when something goes wrong—and it inevitably will—we will all be better prepared to handle it. More importantly, these safeguards will be able to protect those who are most vulnerable and unable to protect themselves.

As we continue to navigate the complex landscape of AI, let’s remember to stay grounded, to focus on the tangible and immediate impacts of our work, and to always strive for the responsible and ethical use of AI. Thank you.

This is a ChatGPT-generated summary of a noisy transcript of a keynote presented at Marist College on Tuesday, June 13, 2023, at 9 am as part of the Enterprise Computing Conference in Poughkeepsie, New York.

The White House Promotes Responsible AI: Here’s What It Means for the Industry

Today, we are on the brink of a new era where Artificial Intelligence (AI) promises to transform every aspect of our lives. However, with great power comes great responsibility. As AI permeates our societies and economies, ensuring its responsible use becomes more critical.

The Biden-Harris Administration has recently taken considerable steps to foster responsible AI innovation, protect citizens’ rights, and ensure safety. But what does this mean for industry players, particularly those who integrate AI into their products?

A Call for Responsibility

At the heart of the White House’s strategy is the principle that companies have a fundamental responsibility to ensure their products are safe before deployment. This safety-first approach means preventing harm and actively promoting the public good.

Meeting with CEOs of leading AI innovators, the Administration has underscored the importance of ethical and trustworthy AI systems, emphasizing safeguards to mitigate risks and potential harms.

Investing in Responsible AI

The Biden-Harris Administration is backing up its words with actions, committing $140 million to launch seven new National AI Research Institutes. These will bring the total number of institutes to 25 nationwide, and each is committed to ethical, responsible AI that serves the public good.

The Role of the NSF IUCRC, Center for Standards and Ethics in AI (CSEAI)

This is where our initiative, the NSF IUCRC, Center for Standards and Ethics in AI (CSEAI), comes into play. We align perfectly with the Administration’s vision for responsible AI, focusing on establishing standards and ethics that serve as the bedrock for AI development and application.

By joining and funding the CSEAI, industry members will directly collaborate with other industry members and with academia, contributing to responsible AI research and advancement. This aligns with the White House’s call for ethical and safe AI and benefits companies in the long run, ensuring their products meet the highest ethical and safety standards.

What this Means for AI Developers and Users

The White House’s recent announcements signal a shift towards more stringent AI development and use standards. This means industries must prioritize building and deploying AI systems that are ethical, trustworthy, and serve the public good.

In a world where AI is becoming ubiquitous, failing to meet these standards can lead to reputational damage, regulatory penalties, and even legal liability. Conversely, those who embrace these principles stand to gain significant competitive advantage, building trust with users and staying ahead of the regulatory curve.

Conclusion

The White House’s commitment to responsible AI is not just good news for Americans—it’s a call to action for industry members who develop or use AI. By aligning with the principles of responsible AI and supporting initiatives like the NSF IUCRC, Center for Standards and Ethics in AI (CSEAI), industry players can meet their ethical obligations and secure their future place in AI.

Join us in making AI safe, ethical, and beneficial for all.

How to Show that Your Model is Better: A Basic Guide to Statistical Hypothesis Testing

Do you need help determining which machine learning model is superior? This post presents a step-by-step guide using basic statistical techniques and a real case study! 🤖📈 #AIOrthoPraxy #MachineLearning #Statistics #DataScience

When employing Machine Learning to address problems, our choice of a model plays a crucial role. Evaluating models can be straightforward when performance disparities are substantial, for example, when comparing two large-language models (LLMS) on a masked language modeling (MLM) task with 71.01 and 28.56 perplexity, respectively. However, if differences among models are minute, making a solid analysis to discern if one model is genuinely superior to others can prove challenging.

This tutorial aims to present a step-by-step guide to determine if one model is superior to another. Our approach relies on basic statistical techniques and real datasets. Our study compares four models on six datasets using one metric, standard accuracy. Alternatively, other contexts may use different numbers of models, metrics, or datasets. We will work with the tables below that show the properties of the datasets and the performance of two baseline models and two of our proposed models, for which we hope to show that they are better, which would be our hypothesis to be tested.

Summary of performance measured with standard accuracy

Summary of the main properties of the datasets considered in this tutorial.

One of the primary purposes of statistics is hypothesis testing. Statistical inference involves taking a sample from a population and determining how well the sample represents the population. In hypothesis testing, we formulate a null hypothesis, $H_0$ , and an alternative hypothesis, $H_A$ , based on the problem (comparing models). Both hypotheses must be concise, mutually exclusive, and exhaustive. For example, we could say that our null hypothesis is that the models perform equally, and the alternative could mean that the models perform differently.

Why is the ANOVA test not a good alternative?

The ANOVA (Analysis of Variance) test is a parametric test that compares the means of multiple groups. In our case, we have four models to compare with six datasets. The null hypothesis for ANOVA is that all the means are equal, and the alternative hypothesis is that at least one of the means is different. If the $p-$ value of the ANOVA test is less than the significance level (usually 0.05), we reject the null hypothesis and conclude that at least one of the means is different, i.e., at least one model performs differently than the others. However, ANOVA may not always be the best choice for comparing the performance of different models.

One reason for this is that ANOVA assumes that the data follows a normal distribution, which may not always be the case for real-world data. Additionally, ANOVA does not take into account the difficulty of classifying certain data points. For example, in a dataset with a single numerical feature and binary labels, all models may achieve 100% accuracy on the training data. However, if the test set contains some mislabeled points, the models may perform differently. In this scenario, ANOVA would not be appropriate because it does not account for the difficulty of classifying certain data points.

Another issue with ANOVA is that it assumes that the variances of the groups being compared are equal. This assumption may not hold for datasets with different levels of noise or variability. In such cases, alternative statistical tests like the Friedman test or the Nemenyi test may be more appropriate.

Friedman test

The Friedman test is a non-parametric test that compares multiple models. In our example, we want to compare the performance of $k=4$ different models, i.e., two baseline models, Gabor randomized, and Gabor repeated, on $N=6$ datasets. First, the test calculates the average rank of each model’s performance on each dataset, with the best-performing model receiving a rank of 1. The Friedman test then tests the null hypothesis, $H_0$ , that all models are equally effective and their average ranks should be equal. The test statistic is calculated as follows:

(1) $\begin{equation*} \chi_{F}^{2}=\frac{12 N}{k(k+1)}\left[\sum_{j=1}^{k} R_{j}^{2}-\frac{k(k+1)^{2}}{4}\right] \end{equation*}$

where $R$ is the average ranking of each model.

The test result can be used to determine whether there is a statistically significant difference between the performance of the models by making sure that $\chi_{F}^{2}$ is not less than the critical value for the $F$ distribution for a particular confidence value $\alpha$ . However, since $\chi_{F}^{2}$ could be too conservative, we also calculate the $F_F$ statistic as follows:

(2) $\begin{equation*} F_{F}=\frac{(N-1) \chi_{F}^{2}}{N(k-1)-\chi_{F}^{2}}. \end{equation*}$

Based on the critical value, $F_{F}$ , and $\chi_{F}^{2}$ , we evaluate $H_0$ ; once the null hypothesis is rejected, we apply a posthoc test. For this, we use the Nemenyi test to establish whether models differ significantly in their performance.

We will start the process of getting this test done by ranking the data. First, we can load the data and verify it with respect to the table shown earlier.

import pandas as pd
import numpy as np

data = [[0.8937, 0.8839, 0.9072, 0.9102],
        [0.8023, 0.8024, 0.8229, 0.8238],
        [0.7130, 0.7132, 0.7198, 0.7206],
        [0.5084, 0.5085, 0.5232, 0.5273],
        [0.2331, 0.2326, 0.3620, 0.3952],
        [0.5174, 0.5175, 0.5307, 0.5178]]

model_names = ['Glorot N.', 'Glorot U.', 'Random G.', 'Repeated G.']

df = pd.DataFrame(data, columns=model_names)

print(df.describe())  #<- use averages to verify if matches table

Output:

       Glorot N.  Glorot U.  Random G.  Repeated G.
count   6.000000   6.000000   6.000000     6.000000
mean    0.611317   0.609683   0.644300     0.649150
std     0.240422   0.238318   0.206871     0.200173
min     0.233100   0.232600   0.362000     0.395200
25%     0.510650   0.510750   0.525075     0.520175
50%     0.615200   0.615350   0.625250     0.623950
75%     0.779975   0.780100   0.797125     0.798000
max     0.893700   0.883900   0.907200     0.910200

Next, we rank the models and get their averages like so:

data = df.rank(1, method='average', ascending=False)
print(data)
print(data.describe())

Output:

   Glorot N.  Glorot U.  Random G.  Repeated G.
0        3.0        4.0        2.0          1.0
1        4.0        3.0        2.0          1.0
2        4.0        3.0        2.0          1.0
3        4.0        3.0        2.0          1.0
4        3.0        4.0        2.0          1.0
5        4.0        3.0        1.0          2.0

       Glorot N.  Glorot U.  Random G.  Repeated G.
count   6.000000   6.000000   6.000000     6.000000
mean    3.666667   3.333333   1.833333     1.166667
std     0.516398   0.516398   0.408248     0.408248
min     3.000000   3.000000   1.000000     1.000000
25%     3.250000   3.000000   2.000000     1.000000
50%     4.000000   3.000000   2.000000     1.000000
75%     4.000000   3.750000   2.000000     1.000000
max     4.000000   4.000000   2.000000     2.000000

With this information, we can expand our initial results table to show the rankings by dataset and the average rankings across all datasets for each model.

Now that we have the rankings, we can proceed with the statistical analysis and do the following:

(3) $\begin{align*} \chi_{F}^{2}&=\frac{12 \cdot 6}{4 \cdot 5}\left[\left(3.66^2+3.33^2+1.83^2+1.16^2\right)-\frac{4 \cdot 5^2}{4}\right] \nonumber \\ &=15.364 \nonumber \end{align*}$

(4) $\begin{equation*} F_{F}=\frac{5 \cdot 15.364}{6 \cdot 3-15.364}=29.143 \nonumber \end{equation*}$

The critical value at $\alpha=0.01$ is 5.417. Thus, because the critical value is below our statistics obtained, we reject $H_0$ with 99% confidence.

The critical value can be obtained from any table that has the F distribution. In the table the degrees of freedom across columns (denoted as $df_1$ ) is $k-1$ , that is the number of models minus one; the degrees of freedom across rows (denoted as $df_2$ ) is $(k-1)\times(N-1)$ , that is, the number of models minus one, times the number of datasets minus one. In our case this is $df_1=3$ and $df_2=15$ .

Nemenyi Test

The Nemenyi test is a post-hoc test that compares multiple models after a significant result from Friedman’s test. The null hypothesis for Nemenyi is that there is no difference between any two models, and the alternative hypothesis is that at least one pair of models is different.

The formula for Nemenyi is as follows:

$CD = q_{\alpha} \sqrt{\frac{k(k+1)}{6N}}$

where $q_{\alpha}$ is the critical difference of the Studentized range distribution at the chosen significance level and $k$ is the number of groups. The $q_{\alpha}$ value can be obtained from the following table:

Critical values for the Nemenyi test, which is conducted following the Friedman test, with two-tailed results.

Thus, for our particular case study, the critical differences are:

(5) $\begin{equation*} CD_{\alpha=0.05}=2.569 \sqrt{\frac{4 \cdot 5}{6 \cdot 6}} = 1.915 \nonumber \end{equation*}$

(6) $\begin{equation*} CD_{\alpha=0.10}=2.291 \sqrt{\frac{4 \cdot 5}{6 \cdot 6}} = 1.708 \nonumber \end{equation*}$

Since the difference in rank between the randomized Gabor and baseline Glorot normal is 1.83 and is less than the $CD_{\alpha=0.10}=1.708$ , we conclude Gabor is better. Similarly, since the difference in rank between the fixed Gabor and baseline Glorot uniform is 2.17 and is less than the $CD_{\alpha=0.05}=1.915$ , we conclude that Gabor is better. Yes, there is sufficient statistical evidence to show that our model is better with high confidence.

Things we would like to see in papers

First of all, it would be nice to have a complete table that includes the results of the statistical tests as part of the caption or as a footnote, like this:

Second of all, graphics always help! A simple and visually appealing diagram is a powerful way to represent post hoc test results when comparing multiple classifiers. The figure below, which illustrates the data analysis from the table above, displays the average ranks of methods along the top line of the diagram. To facilitate interpretation, the axis is oriented so that the best ranks appear on the right side, which enables us to perceive the methods on the right as superior.

Comparison of all models against each other with the Nemenyi test. Models not significantly different at α = 0.10 or α = 0.05 are connected.

When comparing all the algorithms against each other, the groups of algorithms that are not significantly different are connected with a bold solid line. Such an approach clearly highlights the most effective models while also providing a robust analysis of the differences between models. Additionally, the critical difference is shown above the graph, further enhancing the visualization of the analysis results. Overall, this simple yet powerful diagrammatic approach provides a clear and concise representation of the performance of multiple classifiers, enabling more informed decision-making in selecting the best-performing model.

Main Sources

The statistical tests are based on this paper:

Demšar, Janez. “Statistical comparisons of classifiers over multiple data sets.” The Journal of Machine learning research 7 (2006): 1-30.

The case study is based on the following research:

Rai, Mehang. “On the Performance of Convolutional Neural Networks Initialized with Gabor Filters.” Thesis, Baylor University, 2021.

President’s Executive Order for Advancing Racial Equity in AI Systems: What It Means for the Future of AI-Based Technology

Summary: The President of the United States, Joe Biden, has recently authorized an Executive Order intending to enhance racial equity and foster support for marginalized communities via the federal government. The Order mandates that federal agencies employing artificial intelligence (AI) systems assume novel equity responsibilities and instructs them to forestall and rectify any form of discrimination, including safeguarding the public from the perils of algorithmic discrimination.

What you should know: The recent Executive Order on Further Advancing Racial Equity and Support for Underserved Communities Through The Federal Government emphasizes the importance of advancing equity for all, including communities that have long been underserved, and addressing systemic racism in the US policies and programs. This order implies that AI systems should be designed to ensure that they do not perpetuate or exacerbate inequities and should be used to address the unfair disparities faced by underserved communities. It is also implied that the Federal Government should work with civil society, the private sector, and State and local governments to redress unfair disparities and remove barriers to Government programs and services, which could be facilitated by the development and deployment of ethical and responsible AI systems. Additionally, the order emphasizes the need for evidence-based approaches to equitable policymaking and implementation, which can be achieved through collecting and analyzing data on the impacts of AI systems on different communities. Therefore, AI practitioners should ensure that their systems are designed, developed, and deployed to promote equity, fairness, and inclusivity and are aligned with the Federal Government’s commitment to advancing racial equity and supporting underserved communities.

The Center for Standards and Ethics in Artificial Intelligence (CSEAI)

Following President’s Executive Order, we at the CSEAI recognize the critical role of artificial intelligence in promoting fairness, accountability, and transparency. As a research center committed to developing responsible AI techniques, we believe our work can help meet the challenges and opportunities of emerging regulation, standardization, and best practices in AI systems. We are inviting industry members to partner with us financially and take part in collaborative research on trustworthy AI. Our mission is to provide applicable, actionable, standard practices in trustworthy AI and train a workforce that enables fairness, accountability, and transparency. We believe our work will help mitigate AI adoption’s operational, liability, and reputation risks.

The CSEAI brings together leading universities to conduct collaborative research in responsible AI techniques. We are committed to workforce development and providing accessible standards, best practices, testing, and compliance. We are proud to be a part of the NSF IUCRC Program and are excited to be supported by the NSF, which provides a standard agreement, organizational, and legal framework.

Join us in creating a better future for all Americans by developing responsible AI practices that promote fairness, accountability, and transparency. By partnering with the CSEAI, you will have the opportunity to work with a dedicated team of researchers, participate in cutting-edge research, and help shape the future of AI. Contact us today to learn more about partnering with the CSEAI.

Contact Pablo_Rivas@Baylor.edu and find out more at www.cseai.center.

(Editorial) Emerging Technologies, Evolving Threats: Next-Generation Security Challenges

The Volume 3, Issue 3, of the IEEE Transactions on Technology and Society is officially published with great contributions regarding security challenges posed by emerging technologies and their effects on society.

Our editorial piece is freely accessible and briefly introduces the research on this issue and how relevant these issues are. Our discussion briefly discusses GPT and DALEE, as means to show the great advances of AI and some ethical considerations around those. Take a look:

T. Bonaci, K. Michael, P. Rivas, L. J. Robertson and M. Zimmer, “Emerging Technologies, Evolving Threats: Next-Generation Security Challenges,” in IEEE Transactions on Technology and Society, vol. 3, no. 3, pp. 155-162, Sept. 2022, doi: 10.1109/TTS.2022.3202323.

NSF Award: Center for Standards and Ethics in Artificial Intelligence (CSEAI)

IUCRC Planning Grant

Baylor University has been awarded an Industry-University Cooperative Research Centers planning grant led by Principal Investigator Dr. Pablo Rivas.

The last twenty years have seen an unprecedented growth of AI-enabled technologies in practically every industry. More recently, an emphasis has been placed on ensuring industry and government agencies that use or produce AI-enabled technology have a social responsibility to protect consumers and increase trustworthiness in products and services. As a result, regulatory groups are producing standards for artificial intelligence (AI) ethics worldwide. The Center for Standards and Ethics in Artificial Intelligence (CSEAI) aims to provide industry and government the necessary resources for adopting and efficiently implementing standards and ethical practices in AI through research, outreach, and education.

CSEAI’s mission is to work closely with industry and government research partners to study AI protocols, procedures, and technologies that enable the design, implementation, and adoption of safe, effective, and ethical AI standards. The varied AI skillsets of CSEAI faculty enable the center to address various fundamental research challenges associated with the responsible, equitable, traceable, reliable, and governable development of AI-fueled technologies. The site at Baylor University supports research areas that include bias mitigation through variational deep learning; assessment of products’ sensitivity to AI-guided adversarial attacks; and fairness evaluation metrics.

The CSEAI will help industry and government organizations that use or produce AI technology to provide standardized, ethical products safe for consumers and users, helping the public regain trust and confidence in AI technology. The center will recruit, train, and mentor undergraduates, graduate students, and postdocs from diverse backgrounds, motivating them to pursue careers in AI ethics and producing a diverse workforce trained in standardized and ethical AI. The center will release specific ethics assessment tools, and AI best practices will be licensed or made available to various stakeholders through publications, conference presentations, and the CSEAI summer school.

Both a publicly accessible repository and a secured members-only repository (comprising meeting materials, workshop information, research topics and details, publications, etc.) will be maintained either on-site at Baylor University and/or on a government/DoD-approved cloud service. A single public and secured repository will be used for CSEAI, where permissible, to facilitate continuity of efforts and information between the different sites. This repository will be accessible at a publicly listed URL at Baylor University, https://cseai.center, for the lifetime of the center and moved to an archiving service once no longer maintained.

Lead Institutions

The CSEAI is partnering with Rutgers University, directed by Dr. Jorge Ortiz, and the University of Miami, directed by Dr. Daniel Diaz. The Industry Liaison Officer is Laura Montoya, a well-known industry leader, AI ethics advocate, and entrepreneur.

The three institutions account for a large number of skills that form a unique center that functions as a whole. Every faculty member at every institution brings a unique perspective to the CSEAI.

Baylor Co-PIs: Academic Leadership Team

The Lead site at Baylor is composed of four faculty that serve at different levels, having Dr. Robert Marks as the faculty lead in the Academic Leadership Team, working closely with PI Rivas in project execution and research strategic planning. Dr. Greg Hamerly and Dr. Liang Dong strengthen and diversify the general ML research and application areas, while Dr. Tomas Cerny expands research capability to the software engineering realm.

Collaborators

Dr. Pamela Harper from Marist College has been a long-lasting collaborator of PI Rivas in matters of business and management ethics and is a collaborator of the CSEAI in those areas. On the other hand, Patricia Shaw is a lawyer and an international advisor on tech ethics policy, governance, and regulation. She works with PI Rivas in developing the AI Ethics Standard IEEE P7003 (algorithmic bias).

Workforce Development Plan

The CSEAI is planning to develop the workforce in many different avenues that include both undergraduate and graduate student research mentoring as well as industry professionals continuing education through specialized training and ad-hoc certificates.

Special Issue “Standards and Ethics in AI”

Upcoming Rolling Deadline: May 31, 2022

Dear Colleagues,

There is a swarm of artificial intelligence (AI) ethics standards and regulations being discussed, developed, and released worldwide. The need for an academic discussion forum for the application of such standards and regulations is evident. The research community needs to keep track of any updates for such standards, and the publication of use cases and other practical considerations for such.

This Special Issue of the journal AI on “Standards and Ethics in AI” will publish research papers on applied AI ethics, including the standards in AI ethics. This implies interactions among technology, science, and society in terms of applied AI ethics and standards; the impact of such standards and ethical issues on individuals and society; and the development of novel ethical practices of AI technology. The journal will also provide a forum for the open discussion of resulting issues of the application of such standards and practices across different social contexts and communities. More specifically, this Special Issue welcomes submissions on the following topics:

AI ethics standards and best practices;
Applied AI ethics and case studies;
AI fairness, accountability, and transparency;
Quantitative metrics of AI ethics and fairness;
Review papers on AI ethics standards;
Reports on the development of AI ethics standards and best practices.

Note, however, that manuscripts that are philosophical in nature might be discouraged in favor of applied ethics discussions where readers have a clear understanding of the standards, best practices, experiments, quantitative measurements, and case studies that may lead readers from academia, industry, and government to find actionable insight.

Dr. Pablo Rivas
Dr. Gissella Bejarano
Dr. Javier Orduz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open-access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI’s English editing service prior to publication or during author revisions.