Organization:
Hochschule für Wirtschaft Zürich (HWZ), Lagerstrasse 5, 8004 Zürich
Today’s fast-paced and globalized business environment requires firms to regularly update their business models, organizational structures, products, and business processes to remain competitive. In adapting to their changing business environments, firms must engage in structured programs to manage these organizational changes. However, those change programs often fail; failure rates up to 70-80% are reported in the change literature.
ChangeCompanion is thus a chatbot interacting with employees with the purpose to support, explain and make sense in times of organizational change.
We are searching for industry partners interesting in supporting i) implementation, and/or testing/using the developed MVP.
In the world, at least 1 out of 10 children has a disability. The prevalence of neurodevelopmental disorders (NDD), such as autism spectrum disorders, attention deficit disorders, intellectual challenges, learning disabilities, and so on, is anywhere between 7% and 17% (Zablotsky, 2019; cited by Peyre, 2023). 140’000 Swiss children could be struggling with daily life activities right now.
Children with an NDD often maintain their symptoms into adulthood. However, help and care are not offered once they reach 18. This lack of care has a significant impact on their health and well-being. Moreover, the socio-economic impact of NDD is important, even if we are in some of the most prosperous times. Adults with disabilities are less employed and at financial disadvantage compared to neurotypical people. This is unsustainable. A key factor to improve all of this is early detection and treatment, which improve social functioning, education achievement, economic prospects, and reduce the cost on society.
The idea is to develop an app with an AI powered chatbot to offer Occupational therapy suggestions to parents of children with neurodevelopmental disorders (NDD). There is a long waiting list for therapists, so we aim to offer a momentary solution by way of written/video educating on how to perform a certain activity. We will use cognitive science, reeducation, or rehabilitation science to create the perfect “pre-therapy,” focused on daily life activities and education.
Looking for:
We are committed to the UN sustainable developmental goals and would love insights from experienced scientists to help bring our project to life. We can already attest of interest by other OT’s in Romandie and the probable investment of researchers from the department of OT in HETSL through preliminary research, under the condition that we receive the aimed Innosuisse funding. Indeed, to offer the best care, one motivated founder and her team composed of one IT student and one philosophy student are not enough. We need many people, including YOU, as partners, mentors, coaches, and scientists who can bring the best out of this program.
At this stage of the idea, we are mainly looking for industry and practice partners – supporting implementation, for testing or willing to become one of our first customers! 🙂
Short description: Generative AI needs to be tested for safety and security. With the advances of automated testing, the question remains whether humans (including experts) can understand and act on the automated test results. We are interested in Swiss organizations including NGOs and companies, of both small and big size having real-world experience with Human-In-The-Loop. We are also interested in academic partners with expertise in HCI or security frameworks or having a general background in the proposed topic.
Call owner: Dr. Rebecca Balebako – Privacy Engineer – We envision starting the project/the work as soon as possible in 2024. If you are interested, fill out this form.
Requested partners: Academia, Industry, Public
Proposed challenge: AI apps and models need to be tested for safety and security concerns. Current efforts are underway to understand whether safety and security tests can be generated and run automatically. Existing automated testing tools can use LLMs and generative AI. While this allows for more thorough testing, it can also generate a deluge of data. When security teams are overwhelmed by data, it can be hard to cull through to find the pressing and concerning issues. This hinders effective responses. While automated security tests can identify vulnerabilities in AI apps and models, little work has been done to consider the human element in the security review process.
This project aims to address this critical gap by:
Building a community of experts: Bringing together academics, industry practitioners, and security professionals to share knowledge and experiences of human-in-the-loop for AI safety testing.
Developing human-centric guidelines: Creating best practices for designing tools and interpreting test results that prioritize human experience, allowing them to respond to AI test results and security warnings.
Establishing measurable outcomes: Defining metrics to assess the effectiveness of tools and human performance in handling AI test results
Background:
The rapid advancement of artificial intelligence (AI) presents unprecedented opportunities for societal and economic progress. However, the potential risks cannot be ignored. This proposal focuses on the challenge of helping humans (developers, security experts and testers) fix and respond to automated security tests of AI. The list of potential harms from generative AI includes (but is not limited to) prompt injection, privacy leaks, toxic responses, bias, and misinformation. The rapid advancement of LLMs has outpaced the development of effective safety testing methodologies. AI red teaming has emerged as one potential option for testing against specific harms. Red teaming is a term borrowed from cybersecurity, and before that, the US military. Red teams run adversarial tests on the AI model or agent and evaluate whether the response was safe or appropriate.
Automated LLM red-teaming can use generative AI to create adversarial tests, and to assess the security and robustness of the response. For example, red team LLMs can generate many variations of adversarial queries using different formulations or languages. Using LLMs to generate and evaluate queries allows the tests to scale. Furthermore, models can also score the results of the tests, deciding whether a model output was toxic, or leaked PII.
Several tech companies are already building and testing the automated red-team approach. Additionally, several companies are providing LLMs or test suites automated testing including: Microsoft PyRIT, Zurich-based Lakera.AI, and Paris-based Giskard.AI. All of these efforts emphasize the need for human reviewers; none claim that the automated tests alone are sufficient.
However, automated testing tools demand significant human effort to analyze and interpret. This information overload can lead to missed critical issues. Humans need good information and good design to fix security. When security experts get too many alerts, they cannot respond to them (called alert fatigue). The result may be that the extensive testing, but missed opportunities to find and fix issues.
There are no standards or best practices for the usability of AI safety testing. More work is needed on the human element of the automated red team tests. This proposal focuses on understanding the role of human reviewers and security workers in understanding and analyzing the results from automated “red teaming” and automated AI audits.
Project Method and Goals: By focusing on the human-machine interface, this project will enhance the ability of organizations to effectively manage AI risks and ensure system security.
Method: Establish a community of experts to facilitate conversation. Assemble relevant
players in the space, focused on those in Switzerland and Europe.
Include academics and industry experts in AI security, usable security, and HCI.
Get feedback from existing companies and consultants deploying or running AI safety tests.
Use a virtual, asynchronous community to develop a shared vocabulary for this issue.
Potentially host a workshop to discuss what solutions exist for humans who need to analyze and respond to automated red-team or responsible AI results.
Outcome: Write and share guidelines for best practices for including humans in the security and safety testing results.
Create best practices for designing tools to help people respond to AI tests
Define metrics can be used for measuring the usability of developer or security responder experience? Alternatively, what metrics should be used to understand human fatigue and likelihood of missing results.
If you are interested in participating, please reply latest by October 25th, 2024 and send us the feedback form attached, stating
What is the competency that you could bring in?
Do you have specific experience that might be relevant in the project context?
What is the contribution to the project goals that you want to bring in?
The next steps will be:
Based on your proposed contributions, the call owner will decide about the partners to continue on shaping the idea.
Afterward: an additional workshop or/and definition of the concrete implementation plan.
The rules of the game: Decisions on whom to invite for the first meeting, and whom to select for the workshops and final innovation team will be made by the organization, based on the provided information. The goal is to set up an optimal innovation team for reaching the goals, not to create a team with as many partners as possible.
Short Description: Take the opportunity to focus for a full day on your current challenges in the field of Smart Mobility and develop ideas for innovative solutions together with experts and like-minded professionals.
Good to know Participate with two to four people from your SME, start-up, or NGO. Together with up to four other organizations, you will benefit from an innovation workshop with professional facilitation. By harnessing the power of network intelligence, you will be able to develop innovative ideas to solve your challenges in the field of Smart Mobility.
Promising ideas from this innovation workshop may receive financial support and assistance for further development afterward.
This event is funded and organized by Die Mobiliar, in collaboration with the Innosuisse Innovation Boosters Databooster and Artificial Intelligence. Therefore, participants only need to cover their travel expenses.
Interested? Apply by August 30, 2024, with a brief profile of your organization and your motivation for participating via email to info.office@data-innovation.org.
Short Description: We would like to develop an applied research project addressing the following two research questions: i) What are the ethical, environmental, and social responsibilities of individual actors within the AI value chain?, and ii) How can Swiss companies successfully manage their ethical, environmental, and social responsibilities within the AI value network?
To answer such questions, we set three objectives:
1) Analyzing the AI value creation networks of Swiss companies.
2) Developing a concept that encompasses the requirements for ethical, environmental, and social responsibility within the AI value creation network.
3) Prototyping a consulting service including a maturity model and a training concept that will provide a comprehensive approach to responsibility in the AI value creation network.
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