Australian National University

Wenyi Pi

**Addressing Misinformation, Bias, and Privacy Concerns**

<figure>
<img
src="https://cdn-images-1.medium.com/max/914/1*iwEpHWXGnSb8tr0--K-ThA.jpeg" />
<figcaption>Image generated using <a
href="https://www.canva.com/">https://www.canva.com/</a></figcaption>
</figure>

### Author:

- **Wenyi Pi (ORCID:**
 [**0009--0002--2884--2771**](https://orcid.org/0009-0002-2884-2771)**)**

Recently, the spread of false or misinformation has become an important
concern for people all over the world, especially in the era of big data
and multimedia, where people are faced with a flood of information that
makes it difficult to determine its authenticity. According to a recent
Pew Research Center survey, about 64% of adults are affected by false or
inaccurate information. Artificial Intelligence (AI) tools have achieved
great success in a variety of applications and have quickly attracted a
wide user base. Its integration with Bing search engine and other
platforms has gradually integrated it into people's daily lives and work
and further intensified its social impact. With the proliferation of
artificial intelligence (AI) tools, these issues become even more
prominent, as AI-generated text makes it easier to create fake news or
lies, meaning people can produce more disinformation with the same
amount of time and effort.

Researchers have spent great effort to stop the spread of fake
information such as using detectors to distinguish machine-generated
content from human-written context, watermarking AI-generated text to
alert its potential falsity and injecting ethical standards and social
norms to push AI tools away from generating toxic content. Unlike
human-written text which shows diversity in word choices, AI tools are
trained on human-written text and use statistical methods and machine
learning algorithms to generate text. These models learn from word
structure, grammar, and patterns as well as the context of previous
words to predict the next word in a sequence based on its frequency and
proximity.

This article aims to explore the ethical and social Impact of Artificial
Intelligence, focusing specifically on its negative impact on
truthfulness. Furthermore, the article will provide recommendations to
address these challenges.

AI tools use data to train predictions. It can raise a wide range of
potential ethical questions. There are three main issues and several
other issues.

### **Issues of fairness and biassed decisions**

According to the UNESCO Recommendations, "AI actors should avoid
discriminatory or biased applications and outcomes throughout the life
cycle of AI systems, ensuring social justice and fairness". However,
research shows that AI models often overlook important considerations
and tend to rely on irrelevant words due to the inadequacies of training
data. Moreover, they may rely on gender and ethnic biases to make
decisions that violate ethical and legal requirements. Biases are
inherent in the data collection, classification, and usage processes,
compounded by the potential biases introduced by researchers and
developers during algorithm design. If the researchers are racist,
prejudice will affect their judgments unfairly. Developers need to be
aware of the potential harm this bias can cause to their stakeholders.
Users of AI systems may also introduce biases like generating
information for self-benefit and attacking others' interests, leading to
adverse consequences. Researchers may classify an area as high-risk.
However, because they are prejudiced against black people, they may have
chosen not to address misinformation in the region, thereby
inadvertently exacerbating the harm. Companies use AI tools to evaluate
job applicants, bias could cause the AI to favour candidates from
privileged backgrounds over equally qualified candidates from
marginalised groups. These biases in the decision-making process
perpetuate systemic inequalities and have harmful effects on the
individuals affected by them.

### Issues of transparency and explainability

The UNESCO Recommendations emphasise the importance of transparency and
explainability to ensure respect, protection, and promotion of human
rights, fundamental freedoms, and ethical principles. Du et al., 2019
highlight a critical limitation of the machine learning system: lack of
transparency behind their decision making processes, leading to users
having little understanding of how models made their particular
decisions. AI tools have complex data processing techniques and
automated decision-making which lack human input and make it difficult
to understand decision-making rationale. For example, researchers use AI
generators to generate information and sources but when they want to
reference this information, they may struggle to find the sources of
generated information. Also, a smart home security system is equipped
with AI algorithms to detect and respond to threats. However, the system
sometimes fails to differentiate between harmless activities and
security threats. For example, if a family member triggers the home
security system when entering the house at night, the system mistakenly
interprets this as a threat of break-in and activates the alarm system
to call the police. This unexpected behaviour could cause significant
inconvenience that frustrates and confuses users. The concerns about the
black-box nature of AI models have hampered their further applications
in society.

### Issues of individual privacy

The right to privacy is foundational for protecting human dignity,
autonomy, and agency and it must be respected, protected, and promoted
through all AI systems' life cycles. However, there are concerns about
the collection and analysis of various amounts of data by AI technology.
Through constant monitoring by devices or derivation of information
through analytics, it seems that personal information is collected
without individual known consent. This is a significant privacy issue.
For example, When you are scrolling through TikTok videos, the system
pushes something that closely resembles your interests, or even your
thoughts or ongoing activities, such as pushing something related to
your favourite travel or food, it is very worrying. This situation makes
users feel as if their own behaviour and interests are being controlled
by ubiquitous surveillance, and personal privacy is lost. There are also
data leakage related privacy issues. According to Guidelines (Oaic,
2023): AI systems may have security vulnerabilities or be hacked,
resulting in users' personal data being leaked or used for malicious
purposes. For example, individuals share their photos, bank account
details, mobile phone, home address on the social platform. However, if
these social platforms are attacked by hackers, users' personal
information could be leaked.

### Other Issues

Indeed, besides the three main issues, there are other ethical issues.
Especially the principle of proportionality and the principle of doing
no harm in the context of artificial intelligence. Recently, a widely
used AI-powered face-swapping video technology has led to a huge
increase in scams. Fraudsters use this technology to simulate a video,
for example simulating a daughter asking for help and money from her
parents, triggering their sympathy and trust, and thereby defrauding
their money. Moreover, there are numerous other scenarios such as fraud
through fake voice synthesis, phishing attacks, and fake news
generation. These unethical behaviours greatly impact human rights and
well-being. They not only infringe on the personal rights of the users,
but also bring financial losses and psychological trauma to the victims.

### Recommendations

To better control the spread of misinformation and fakes, a chatbot
detector software tool can be used. Unlike people's subjective awareness
which may be influenced by biases, This tool uses statistical methods
and machine learning algorithms to analyse text patterns and assess its
possibility of misinformation. Through classification and regression
techniques, the software classifies text as real or fake based on
training examples and predicts the likelihood of encountering
misinformation in a particular circumstance. Powered by neural networks
and deep learning, the tool can identify hidden patterns in sequences of
text and detect synthetic text. Transitioning from manual methods to
advanced automatic software can enhance the capabilities of public
security departments, enabling efficient data storage and analysis. It
is very effective because it can display the results directly, without
the need for further analysis. Moreover, its adaptable algorithm allows
for continuous learning from previous research and streamlines the
entire process, making the whole process more automated and flexible.

To address the problem of biased decision-making, several steps should
be used. Firstly, interpretability to examine whether models are
affected by these biases to ensure that models comply with ethical and
legal requirements. Secondly, it is important to ensure that all input
data provided to the AI is up-to-date and unbiased. Therefore, people
developing AI algorithms should receive appropriate ethical training
courses to prevent their personal racial biases from influencing the
development of algorithms, thus ensuring fairness and objectivity.

The best way to prevent and mitigate harm is to make everything
transparent. Users and analysts of the software should be honest and
professional with the public. They shouldn't try to hide anything from
the public. Institutions should explain the principles of algorithms to
the public to prevent suspicion and maintain trust. Developers could use
deep neural networks (DNN) to achieve global interpretability which can
help people interpret the AI models' inner work mechanisms. They can
also use local interpretability to uncover relationships between input
and relevant model predictions, therefore increasing their transparency
to users.

In order to reduce data privacy exposure, users need to learn about
network information security knowledge and enhance their awareness of
data privacy protection such as using a regular browser, not clicking on
unidentified links, and turning on security protection functions to
prevent viruses and hackers. The platform can use technology to create a
secure data environment, including encrypting user data, implementing
strict access control and monitoring mechanisms, using firewalls,
anti-virus software to protect the security of personal data and ensure
that user data security and privacy are not violated.

In response to the lack of supervision, relevant departments should
strengthen supervision. On the one hand, the public supervision and
reporting channels are unblocked, and once fraud is found, the network
security department stops it in time to reduce the loss, on the other
hand, the use of technical means to reverse supervision, real-time
tracking of criminal suspects, and strengthen publicity. Moreover,
social media platforms should provide more common fraud behaviours and
measures to encounter fraud, such as the specific operation of using
anti-fraud apps, complaints channels, etc., so that the public can
improve anti-fraud prevention.

### **Sources**

- Du, M., Liu, N. and Hu, X. (2019) 'Techniques for interpretable
 machine learning', *Communications of the ACM*, 63(1), pp. 68--77.
 doi:10.1145/3359786.
- Oaic (2023) *Guide to data analytics and the australian privacy
 principles*, *OAIC*. Available at:
 <https://www.oaic.gov.au/privacy/privacy-guidance-for-organisations-and-government-agencies/more-guidance/guide-to-data-analytics-and-the-australian-privacy-principles.>
- Silver, L. (2019) *Misinformation and fears about its impact are
 pervasive in 11 emerging economies*, *Pew Research Center*. Available
 at:
 <https://www.pewresearch.org/short-reads/2019/05/13/misinformation-and-fears-about-its-impact-are-pervasive-in-11-emerging-economies/.>
- Savage, N. (2023) 'The rise of the Chatbots', *Communications of the
 ACM*, 66(7), pp. 16--17. doi:10.1145/3596206.
- Unesco (2023) *Recommendation on the ethics of Artificial
 Intelligence*, *UNESCO.org*. Available at:
 <https://www.unesco.org/en/articles/recommendation-ethics-artificial-intelligence>

![](https://medium.com/_/stat?event=post.clientViewed&referrerSource=full_rss&postId=d5988f70122e){width="1"
height="1"}

Addressing Misinformation, Bias, and Privacy Concerns

Author:

 Wenyi Pi (ORCID:


 0009–0002–2884–2771


 )

Recently, the spread of false or misinformation has become an important concern for people all over the world, especially in the era of big data and multimedia, where people are faced with a flood of information that makes it difficult to determine its authenticity.

The Ethical and Social Impact of Artificial Intelligence

Alice Fleerackers

This July, ScholCommLab's Stefanie Haustein attended the sixth ever
Brazilian Meeting on Bibliometrics and Scientometrics in Rio de Janeiro.
In this short Q&A, she shares highlights from the event, including a
keynote presentation about her work on Twitter and scholarly
communication, connections with researchers from around the world, and a
healthy dose of delicious Brazilian cocktails.

**![](https://www.scholcommlab.ca/wp-content/uploads/2018/07/IMG-5308-768x1024.jpg){.wp-image-954
.alignright loading="lazy" decoding="async" width="350" height="467"
srcset="https://www.scholcommlab.ca/wp-content/uploads/2018/07/IMG-5308-768x1024.jpg 768w, https://www.scholcommlab.ca/wp-content/uploads/2018/07/IMG-5308-225x300.jpg 225w, https://www.scholcommlab.ca/wp-content/uploads/2018/07/IMG-5308.jpg 1536w"
sizes="(max-width: 350px) 100vw, 350px"}Tell me about the Brazilian
Meeting on Bibliometrics and Scientometrics. What's the conference
about? Who attends?**

The EBBC is a biannual national conference, where researchers and
students from all over Brazil meet to present and discuss their findings
on bibliometric and altmetrics research. This year was the sixth time
EBBC took place, and it returned to Rio de Janeiro, where the inaugural
conference was held in 2008. The conference language is Portuguese with
guest speakers presenting in English. I was quite impressed by the
number of presentations and the size of the Brazilian scholarly metrics
community.

**The theme of this year's conference was "Science in Network." In your
view, how do networks affect or enable the way research is done?**

Networks are essential to do research. We've come a long way from the
lone scholar and most research now is carried out by groups of scholars,
which often involve national and international partners. I personally
think that any analysis or publication gets better when it is done in
collaboration, particularly if collaborators come from different
backgrounds and are equipped with different skill sets.

**You gave one of the keynote speech at the conference. Can you tell me,
briefly, what it was about? Did your work at the ScholCommLab inform
your talk?  **

<figure id="attachment_950" class="wp-caption alignleft"
aria-describedby="caption-attachment-950" style="width: 350px">
<img
src="https://www.scholcommlab.ca/wp-content/uploads/2018/07/IMG-5172-e1532638536376-225x300.jpg"
class="wp-image-950" loading="lazy" decoding="async"
srcset="https://www.scholcommlab.ca/wp-content/uploads/2018/07/IMG-5172-e1532638536376-225x300.jpg 225w, https://www.scholcommlab.ca/wp-content/uploads/2018/07/IMG-5172-e1532638536376-768x1024.jpg 768w"
sizes="(max-width: 350px) 100vw, 350px" width="350" height="467" />
<figcaption>Data cleaning with a view: roof top at the hotel with a view
on the Copacabana beach</figcaption>
</figure>

EBBC invited three international speakers. Cassidy Sugimoto opened the
workshop on Tuesday with a talk on science in a global society. Cameron
Neylon and I presented on Wednesday. My talk was entitled H*ow, when and
what does the Twittersphere tweet about science?* and summarized
findings from my work on scholarly Twitter metrics. It was based on
[this book chapter](https://arxiv.org/abs/1806.02201) (which I also
blogged about
[here](https://www.altmetric.com/blog/twitter-in-scholarly-communication/))
and provided data on tweets linking to scientific articles published by
Brazilian authors. Having cleaned geographical data for all tweets from
Brazil (with a view of Copacabana), I could show that the majority of
tweets came from the South and Southeast of Brazil, where most large
cities are located. For the first time I also analyzed emojis used in
those tweets. Juan Alperin helped me to extract those from the tweet
text using an [emoji Python library](https://pypi.org/project/emoji/).
At less than 1%, only a small share of tweets contained emojis, but it
was nevertheless interesting to explore differences between countries.

**What was the highlight of the conference for you?**

The whole week was a highlight so it is hard for me to pick one. Since I
do not speak Portuguese I probably missed a lot of interesting talks by
local presenters, but I had really interesting discussions over coffee
during breaks and over caipirinhas during a social event in a salsa bar.
I particularly enjoyed meeting people in person who I had previously
been connected to on Twitter, sometimes for years. I also managed to
take a day off to go snorkeling in Búzios.

**What did you take away from the event?**

I made a number of new connections at EBBC6, which will hopefully lead
to new collaborations and might even bring new visiting scholars to the
ScholCommLab. After Germana Barata left SFU last month, it would be
great to have another scholar from Brazil come work with us!

I also came home from Rio with a concrete plan to publish the results
from the Brazilian Twitter analysis together with Iara Vidal and Fábio
Castro Gouveia on the Scielo blog. I am pretty excited about the
potential audience, as the blog publishes all posts in English,
Portuguese, and Spanish.

*For more information about the conference, visit* *ebbc.inf.br*.

This July, ScholCommLab’s Stefanie Haustein attended the sixth ever Brazilian Meeting on Bibliometrics and Scientometrics in Rio de Janeiro. In this short Q&amp;A, she shares highlights from the event, including a keynote presentation about her work on Twitter and scholarly communication, connections with researchers from around the world, and a healthy dose of delicious Brazilian cocktails.

Rogue Scholar Posts

The Ethical and Social Impact of Artificial Intelligence

Highlights from the Brazilian Meeting on Bibliometrics and Scientometrics