Week 7 Share -- Ethics of Group Participative Model Building
- What are some of ethical concerns of building group participative models?
- What if a “member” of the group is a generative AI?
WELCOME to System Dynamics 101 - Spring Session I - MBA 645
WELCOME to System Dynamics 101. This blog, THE WALL, will be our vehicle to communicate with one another throughout the 7 weeks of this course. First, of all, please introduce yourselves. Second, post your 2 to 3 person teams. Third, post any questions or concerns you might have. That third point is critical: this forum will serve as our way to grow experience and understanding of the models we will be building and interpreting all term. Be sure to encourage one another daily -- while it is still today! Thanks, Bill
Here ethics asks the question: how ought participants work in groups? Flipping this around: how ought the group treat any one participant? I usually favor the idea that everyone inventory and share their capabilities in a group. Those with more extensive capabilities in one area should help those with less. Should one carry the group? How can others have a voice? Is efficiency the goal of the group? Is the group greater than the sum of the participants? How should conflicts be resolved, decisions be made, work be apportioned?
ReplyDeleteConstructing group participative models raises various ethical concerns that need careful consideration. One significant concern revolves around transparency and informed consent, ensuring that all participants understand the model's purpose, their role, and the potential implications of their involvement. Equitable participation is crucial, as ethical considerations dictate fair inclusion and representation of diverse perspectives within the group. It is essential to guard against bias and discrimination, both in the model's design and in the decision-making processes that arise from it.
ReplyDeleteWhen constructing group participative models, ethical considerations play a pivotal role, especially when incorporating generative AI as a member of the group. Transparency and informed consent are paramount, demanding clear communication regarding the AI's role, capabilities, and potential impact on decision-making. Ensuring equitable participation and fostering inclusivity and diversity within the group are ethical imperatives, allowing all members, human or AI, to contribute based on their unique perspectives. Shared capabilities and responsibilities, as well as mechanisms to empower and give voice to all participants, are crucial for ethical group dynamics. Balancing individual capabilities with the collective synergy of the group is essential, recognizing that the group should be greater than the sum of its parts. Ethical practices involve establishing fair mechanisms for conflict resolution and decision-making, ensuring transparency and impartiality. The ethical debate also revolves around the goal of the group, striking a balance between efficiency and responsible decision-making. Preserving human autonomy while leveraging AI capabilities responsibly is a critical consideration. Finally, continuous evaluation and adaptation are necessary to uphold ethical principles, ensuring that the group remains inclusive, fair, and effective over time.
1) Building group participative models raises several ethical concerns:
ReplyDelete- Inclusivity and Representation: Ensuring that all relevant people are included in the modeling process is essential for fairness and representation. Excluding certain groups or individuals can lead to biased outcomes and undermine the legitimacy of the model.
- Transparency and Accountability: It's crucial to be transparent about the modeling process, including data sources, assumptions, and decision-making criteria. Lack of transparency can erode trust and raise concerns about accountability, especially if the model's outputs have significant impacts on people.
- Power Dynamics: Power imbalances among group members can affect the fairness of decision-making within the modeling process. Dominance by certain people or groups can marginalize others and skew the model's outcomes in favor of the more powerful participants.
2) Regarding the involvement of a generative AI as a "member" of the group, additional ethical considerations arise:
- Agency and Representation: Questions arise regarding the agency and representation of a generative AI within the group. It's essential to clarify the role and limitations of the AI in the decision-making process to ensure that its contributions are understood and appropriately weighted.
- Bias and Fairness: Generative AI systems can inherit biases from their training data or algorithms, potentially influencing the outcomes of the group model. Addressing bias in AI systems and ensuring fairness in their contributions is crucial for ethical decision-making.
- Transparency and Explainability: AI systems, particularly generative models, can be complex and opaque in their operations. Ensuring transparency and explainability of the AI's contributions to the group model is important for understanding its impact and building trust among participants.
1) Building group participative system dynamics models comes with several key ethical considerations around inclusion, power dynamics, and representation. A core challenge is ensuring all voices in the group are heard and incorporated into the modeling process without bias. If certain voices or perspectives dominate, it can skew the resulting model and analysis. Relatedly, we must be aware of power structures within the group and actively mitigate instances where hierarchy silences contributors. Sensitivity around these dynamics is critical for ethical participative modeling.
ReplyDeleteAdditionally, ethical issues arise when determining the problem framing and bounding of the model. Who gets to shape what is modeled and what is excluded? Without diverse input and multiplicity of framing, models can suffer from narrow assumptions and structural bias. Modelers have an ethical duty to bring awareness to what voices are missing and what critical feedback loops may be left out.
2) If one of the “members” helping co-create the model is a generative AI system, further ethical considerations emerge. We must audit the AI’s training data and algorithms to ensure it does not propagate unfair biases or make unsupported causal claims. The interpretable transparency of the AI’s logic must be clear to all group members. There are also deeper questions around whether AI qualitative opinions carry the same weight as human group members in the participative process and what that implies about agency and power. Principles of accountability and transparency must be maintained with an AI modeling contributor, likely necessitating some independent oversight body. Ultimately, while AI assistance enables benefits like quickly mapping causal hypotheses, ethical diligence is critical when ceding modeling decisions to black-box generative algorithms in participative settings.
The ethical application of modeling methodologies should be a central concern as we construct - with both human and AI partners - the complex system simulations that increasingly shape our understanding of the world. Conscious inclusion, representativeness, and accountability pave the way.
1. With multiple people involved in the project, there are also various backgrounds. An ethical concern could be the conflicts of interest that may be present. This leads into transparency. Conflicts of interest are not beneficial to the making of the model because it can lead to disrupted results. Transparency is necessary to maintain the integrity of the results.
ReplyDeleteGoing along with conflicts of interest, we all have inherent biases. With various people building participative models, it is important to be aware of our biases to check ourselves during the design. You don’t want to let your bias impact the result of the simulation.
Lastly, an ethical concern of building group participative models is whether the designers and model are fair and just. You don’t want your model to inherently skew one way to mess with the results. Going back to transparency, you want to approach the build on a level playing field.
2. Ethical concerns arise if a “member” of the group is a generative AI. First, I would be concerned about data privacy when using generative AI. If ChatGPT was used, you cannot guarantee that the data you input will be securely stored. You have to assume that whatever information you give the generative AI can be spread. I would be concerned about the generative AI having access to the data in the model.
Additionally, the accuracy of the information provided by the generative AI may be questionable. This tool pulls information from across the web, so just because it answers your question or prompt doesn’t mean it’s correct. This is especially important as information has to be inputted into the group participative model. You want to ensure the accuracy of the information before the simulations run so that you can make accurate decisions based on that.
Lastly, since generative AI is not an actual human, it has no inherent integrity. This is important when designing a model to predict future scenarios. Honesty is needed to input neutral data to then make an unbiased conclusion after the simulation is run.
What are some ethical concerns of building group participative models?
ReplyDeleteSome of the ethical concerns of building group participative models is the power dynamics. The imbalance of power within members of the group will have a great impact on the decisions and the fairness of those decisions. The model can become skewed with the outcome if some of the members involved are more involved and take over. I also think the idea of accountability is huge and a concern. People must be accountable and transparent about their data presented and decisions that they make.
What if a “member” of the group is a generative AI?
If a “member” of the group is a generative AI some ethical concerns are bias and fairness. When it comes to AI systems the algorithms that they are made up of can create a level of bias from who created them. This can have a massive outcome on the model. You must make sure to look into and address the bias involved in the AI model which is a concern.
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ReplyDelete1. There are multiple ethical concerns of building group participative models. One of these is the hope that no one deflects. When working in a group model, it is imperative that everyone does their part or the model will fail. You have to trust that everyone will remain ethical by doing only their part, nothing more or less, to keep the system afloat. Another concern is the sharing of information. Everyone in the model has to be willing to be a team player. If someone comes up with an idea to benefit the group, then it should be shared so everyone can benefit, not just giving the glory to one part of the project. Alternatively, there are some instances where you may not be allowed to share information, which causes issues as well, so clear boundaries for communication need to be established early on in the modeling process. It is also crucial to ensure diverse representation so there are multiple perspectives being heard. Creating a model in an echo chamber may lead to missing critical details or interesting new ideas since no other perspectives were taken into account. Finally, we all have inherent biases, so we must try to make ourselves aware of them before we build them into our model.
ReplyDelete2. If a “member” of the group is a generative AI, then there are other concerns that need to be considered. Firstly, AI can also have biases, specifically the biases of those who created it. Just like we must self-reflect to recognize our personal biases, AI developers must do the same or the system will be inherently skewed. We have seen examples of this in online job application systems where people believe the model is inherently built against them. Another point of contention is how accurate the information we are receiving from the generative AI is. AI is learning everyday and when you feed a prompt to chat GPT, you are often getting wrong information. For example, when Microsoft launched their first version of AIon Twitter, people flooded the chat box with inappropriate information, making any responses from the AI unusable. Finally, other team members need to recognize that generative AI is not human and has significant boundaries. The uses of AI should be clearly defined so people are not confused on what the AI should and should not be allowed to do in a project.
I love the comments on the AI! We should write a useful guide and policy.
DeleteEthical concerns can be one providing more due diligence and effort than others. For example, if a company model were to split up the model into their respective divisions such as HR, Front office and back office this could be an ethical issue. If one division did not do their part and lets say back office decided to make up for front office, the legality behind this could prove unethical. For one, each branch has confidential information so one division would not have access to this. This could also mean that even if all the branches put the required effort in, each branch has to be careful to not release confidential data to the other branches that are communicated throughout the model. If a member is generative AI, this might be able to prove useful. For one the department can pour the confidential information into the AI which the company can trust if built b y their own developers. The only complication behind is is legality of having an AI in the group. Can AI be considered a team member?
ReplyDelete1. There are some of ethical concerns of building group participative models such as:
ReplyDelete- Lack of the balance in contribution to the study which leads to unfairness of collaboration. Every team member should have similar/equal participation to the study.
- Every team member should be accountable to their part as their finding would impact the result and the interpretation.
- Transparency is another ethical factor that must be considered as it would affect on the delivery of data and eventually on decision-making. Thus, lack of transparency among the team member would make misunderstanding, harm the project and wrongful decision.
- Fair opportunity for each team member and includes each of them to share their ideas and insights that would help to make better decision, prevent any bias decision/approach and reduce confliction.
2- As a generative AI is not a human being and is based on the programming, it cannot be a reliable member of the group since it cannot be accountable to its data. Using generative AI can create a conflict of interest in decisions-making as it cannot realize the application of ethical values in its data.
1. Ethical concerns related to building group participative models include issues of transparency, accountability, and fairness. There may be concerns about ensuring that all participants have equal access to information and opportunities to contribute, as well as the potential for power imbalances or manipulation within the group dynamic. Additionally, there may be questions about the ethical implications of using data or insights obtained from the group without proper consent or consideration for privacy rights.
ReplyDelete2. If a "member" of the group is a generative AI, ethical considerations become even more complex. There may be questions about the AI's ability to truly understand and engage in the group process, as well as concerns about the potential for bias or manipulation in the AI's answers. Additionally, there may be ethical considerations related to the ownership and control of the AI's output, as well as concerns about the impact of its participation on the dynamics and outcomes of the group.
Some ethical concerns of building group participative models include transparency, equality, joint effort, and the challenge of various levels of understanding of the model and information. Everyone in the group is expected to contribute equally to building, running, and understanding the model; however, that is only sometimes the case. Giving everyone a good grade in the group could be unfair if someone puts in less effort than others. Some may not be truthful in how they gather information or credit other people, thus leading to an ethical concern of plagiarism. If there are various levels of understanding, the one with the most understanding may be challenged to do more in the group.
ReplyDeleteIf a group member was generative AI, the entire group model would be compromised ethically. AI has no moral complexes as people do, so nothing stops it from committing unethical behavior such as stealing credit and plagiarizing others' work. AI aims to get the task done or the model to run. It is unreliable, and the information may not always be correct.
1. In a group setting, it's essential for participants to work together by leveraging their unique strengths and acknowledging their limitations. This begins with each member taking inventory of their skills and openly sharing this information, creating an environment where collaboration is valued over competition. Individuals with greater expertise in certain areas should be encouraged to mentor those with less experience, ensuring that knowledge and skills are shared equitably. This dynamic prevents any single person from shouldering the entire project and allows for a more inclusive atmosphere where everyone's voice can be heard. The ultimate aim is not merely efficiency but fostering a group identity that exceeds the capabilities of its individual members, promoting a sense of unity and shared purpose.
ReplyDelete2. When it comes to the group's interaction with individual participants, respect and consideration are paramount. Each member's contributions should be valued, with efforts made to ensure diverse perspectives are not only included but actively sought after to enrich the group's output. Decision-making processes and conflict resolution should prioritize transparency and fairness, with tasks distributed based on ability, interest, and availability rather than hierarchy. Recognizing and mitigating biases, whether in human judgment or AI tools used in the project, is crucial to maintain the integrity and fairness of the group's work. In this way, the group can achieve a balance between harnessing the strengths of its members and ensuring equitable participation, leading to outcomes that are both innovative and representative of the collective effort.
1. One large ethical concern when doing a group project is the amount of time and effort given by each team member to the finished project. When doing a project close to equal effort should be contributed by all parties. There shouldn’t be an overwhelming majority of the project done by one group as this could lead to other group members receiving grades which they did not contribute too. It is also important to eliminate bias when creating a project. projects should be done based on facts and representing a project as an opinion may not be accurate.
ReplyDelete2. A concern I would have with using AI as a group member would be confirming that the information that the AI was generating was accurate. due diligence should be done to make sure whatever information the AI provides you with is accurate. This would involve doing research on the AI portion to make sure all info is correct. Another issue that might apply is that of plagiarism. The AI probably doesn’t have a moral conscious and can’t interrupt what is stolen or not. When providing information it is important that credit is given to those who the information comes from.
1) When it comes to the representation and bias in group participative models, it’s crucial to ensure that all group members are fairly represented. If the AI is trained on data that is biased or unrepresentative of the entire group, it could lead to unfair outcomes or decisions. This is particularly concerning if the AI is making decisions that have significant impacts on the group members. Another concern is the transparency and explainability of the AI’s actions. If the AI’s decision-making process is opaque, it can lead to mistrust among the group members. This is especially important when the AI’s decisions have a direct impact on the group members. The use of AI in group participative models also raises concerns about privacy and data security. The AI might have access to sensitive information about the group members, and there could be risks if this information is not properly secured.
ReplyDelete2) When a generative AI is a member of the group, it raises questions about accountability. If the AI makes a decision that leads to negative outcomes, it’s unclear who should be held responsible. This is a complex issue that touches on legal and ethical considerations. The introduction of AI into a group can alter the power dynamics. If the AI is perceived as an authority or if its suggestions are given more weight, it could marginalize human members or unduly influence the group’s decisions.
Ethical issues in group participative models mainly involve transparency, fairness, and bias. A key concern is conflicts of interest among people with diverse backgrounds, which may affect the model's integrity. Transparency in decision-making is vital to address such conflicts and maintain fairness. Moreover, biases among team members can affect the model's outcomes, highlighting the need to recognize and rectify them for reliable results.
ReplyDeleteWhen incorporating a generative AI in the group, ethical considerations arise regarding its role, representation, and potential biases. It's essential to clearly define the AI's role and limitations in decision-making to ensure fair contributions. Generative AI systems may inherit biases, impacting the group model's outcomes. Addressing bias in AI systems and ensuring transparency in their contributions are crucial for ethical decision-making and building trust among participants. By addressing these ethical issues, we aim to develop group participative models that are credible and ethically sound.
Building group participative models raise several ethical concerns, especially when one of the members of the group is a generative AI. One of these ethical concerns is fair representation. This is important because it answers fair representation of all stakeholders in the modeling process. An AI member may unintentionally carry on biases present in its training data, leading to unfair representation or marginalized of certain perspectives. Another ethical concern has to do with transparency and accountability. The concern with this is that ensuring transparency in how decisions are made and holding individuals or entities accountable for model outcomes. Due to the nature of some AI models, they can not fully explain the reasoning behind their outputs, which makes it potentially compromise transparency and accountability. Another concern has to do with bias and fairness. Mitigating bias in decision-making processes is important to avoid unfair advantages and disadvantages. If an AI member was introduced, they may introduce or amplify biases, which would ultimately impact the fairness of group decisions.
ReplyDeleteThe main ethical concern that arises from a group participative model is the uneven distribution of input. Most groups (if not all) have a skew where some members have more influence on the final product than others, and in many cases, the work of different group members if contained to different aspects of the project. In the example of models, this may cause a lack of synergy or design cohesion in a model, and can cause conflicts of interest or inefficiencies. Transparency is also an issue stemming from this, as the biases and influences of different group members may not be readily apparent, and can detract from the reliability of a model's results and interpretations.
ReplyDeleteThe concept of a generative AI being a member in this sort of group is a very interesting prospect. I think there's something to be said about an AI potentially linking together pieces of a model or project that may otherwise be somewhat disjointed by contributions from several group members. However, an inherent issue with AI (as others have rightly pointed out) is the likelihood that it would inherit biases from those around it, or its source learning material. This evaluation ultimately rests with how the AI was designed and trained; in theory, it could provide a stabilizing or baseline neutral opinion for a group, but on the other hand can easily exacerbate existing biases or inefficiencies based on AI's current level of development. Interesting to think about for the future, though.
1. One of the biggest ethical concerns of building group participative models is transparency. Without transparency, the data can be affected drastically, where this leads to misunderstanding of data. This misunderstanding of data can cause harm to the project since it can make wrong decisions based on incorrect data. Another ethical concern is dynamic teamwork amongst the group that all members of the group have to work together. Each team member has to be accountable for their part and willing to share their ideas and insight with the others.
ReplyDelete2.If a “member: of the group is a generative AI, there are concerns that need to be considered. Since AI is a program based it relies on information that is given to it and stored. If this information is not complete or has biases it can cause the AI to give biased answers. If AI is given data biases can result in the model becoming tainted and producing the wrong results.
1. Ensuring equal participation and representation within the group is essential to avoid marginalizing certain voices or perspectives. Failure to promote inclusivity can lead to ethical issues related to fairness and equity. There should also be transparency regarding the purpose, process, and outcomes of building participative models. Lack of transparency can lead to mistrust among participants and undermine the legitimacy of the model. Additionally, It's essential to establish mechanisms for holding participants accountable for their actions and decisions within the group. Lack of accountability can lead to unethical behavior, such as manipulation or exploitation of the process for personal gain.
ReplyDelete2. If a AI "member" is in the group, similar issues can occur. For example, if the data provided to the AI is skewed, it could potentially be biased on a result due to a limited supply of data. It is often easier to communicate and split work with other people when doing a project because you can delegate tasks to each person accordingly. However, AI might make this more difficult by either not doing enough tasks or overworking and dehumanizing the project/results (most likely overworking).
Constructing group models involving group participation gives rise to ethical considerations, particularly when incorporating a generative AI as a member. One critical ethical concern revolves around ensuring fair representation. This is significant as it addresses the need for fair inclusion of all stakeholders throughout the modeling process. There may be questions about the AI's ability to truly understand and engage in the group process, as well as concerns about the potential for bias or manipulation in the AI's answers. Additionally, there may be ethical considerations related to the ownership and control of the AI's output, as well as concerns about the impact of its participation on the dynamics and outcomes of the group.
ReplyDeleteWhen examining assumptions and interpreting results in a complex system, it's important to consider various horizons and system boundaries to ensure a valid and robust analysis. Understanding Short-Term vs. Long-Term horizons and assessing the implications of assumptions and results over different timeframes.
ReplyDeleteStakeholder Perspectives: Internal vs. External Stakeholders and consider the viewpoints of both internal (employees, management) and external stakeholders (customers, community members). Different perspectives can highlight diverse impacts.
Written by Vetle
ReplyDelete1) Some of the issues related to building participative models, involves protecting privacy and confidentiality, ensure everyone is participating equally, and conflicts of interest
2) The probelm having a generative AI member in your group, could be it shares false information or information that's no longer relevant to what the group is seeking!
Great last point!
DeleteI think that the ethics of working in a group are something that should be left up to the group in terms of finding what works for them. As long as everyone feels respected and valued in a group setting, if they all agree that something works—when some people have struggles while others might be more proficient—they are all helping each other to reach the best possible output. This goes for the same as utilizing AI: AI is an inevitable tool that will continue to shape future generations and the way in which humans think, and it is important to learn how to effectively use this technology for positive benefit for humanity as opposed to negatively. AI (at least for the long term future) will not replace humans' ability to think, it is just a tool that can enhance reasoning skills that might be limited by current human capacities.
ReplyDeleteBuilding group participative models, where decisions are made collectively by a group rather than by a single individual, brings several ethical concerns that need to be carefully addressed. Ensuring that all relevant voices are heard is crucial. Groups may unintentionally marginalize minority perspectives or individuals who are less vocal. In group settings, power imbalances can emerge, where dominant individuals or subgroups may influence decisions disproportionately. This can undermine the fairness of the participative process and lead to outcomes that do not reflect the collective interest. There may also be pressure to conform to the majority view or dominant voices, which can stifle dissent and reduce the diversity of opinions. This pressure can compromise the quality of the decision-making process and ignore minority viewpoints that might be crucial.
ReplyDelete2. Incorporating a generative AI as a member of a participative group introduces a range of unique ethical concerns and considerations. AI systems can reflect biases present in their training data or algorithms. There's a risk that the AI could influence the group’s decisions in a biased way, either by reinforcing existing biases or introducing new ones. The biggest one in my opinion is AI lacks personal experiences and emotions. While it can process data and provide analysis, it doesn’t represent human perspectives. Ensuring that its contributions are balanced with human input is essential to maintain fairness and relevance. AI may have access to sensitive and secure information. Safeguards need to be in place to ensure that this information is handled securely and ethically, respecting participants' privacy.
When it comes to building group participative models involves several ethical concerns. Ensuring fair representation is crucial, as dominant voices may overshadow others, leading to biased results. Privacy and confidentiality must be handled with care to protect sensitive information shared by participants. Accountability for group decisions should be clearly defined to avoid confusion and ensure proper attribution. Additionally, there's a risk of manipulation if certain individuals use their influence to sway the group unfairly.
ReplyDeleteWhen a generative AI is included, further ethical issues emerge. AI can introduce biases based on its training data, impacting the model's fairness and accuracy. Transparency is necessary to understand AI's role and contributions. Clear guidelines are needed to manage the AI’s influence and ensure ethical use. Finally, accountability for decisions involving AI requires careful consideration, as AI itself cannot be held responsible for its actions.