In today’s digital-first world, public relations (PR) is increasingly shaped by algorithms. From AI-powered media monitoring tools to predictive analytics platforms and automated sentiment analysis, algorithms influence how brands listen, what they prioritize, and ultimately, how they communicate.
But as PR professionals grow more dependent on these systems, an uncomfortable question has emerged: Can an algorithm be truly inclusive?
This question is more than academic—it strikes at the core of how brands engage with the world. When algorithms shape messaging strategies, determine whose voices are amplified, or automate responses to crises, inclusivity must be a foundational concern. Yet the very technologies meant to help us connect often risk perpetuating exclusion, silencing marginalized voices, reinforcing dominant narratives, and narrowing the scope of public discourse.
In this blog, we’ll unpack what algorithmic bias means for PR, explore how exclusion happens, and offer actionable steps PR teams can take to build more inclusive, responsible digital strategies.
The Algorithmic Turn in PR
The rise of digital tools has transformed the PR industry. Consider just a few ways algorithms are already embedded in daily practice:
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Media Monitoring Tools (e.g., Meltwater, Cision): Use natural language processing (NLP) to identify brand mentions and public sentiment.
Social Listening Platforms (e.g., Brandwatch, Sprinklr): Rely on machine learning to track emerging conversations and detect trends.
Campaign Optimization Tools: Use predictive analytics to recommend optimal messaging, channels, or posting times.
Content Generation AI: Tools like ChatGPT and other generative AI platforms are now helping teams create pitches, press releases, and social content.
All of this creates efficiencies. But it also means that a growing portion of PR decision-making is no longer purely human—it’s filtered through an algorithmic lens.
The Problem: Bias in, Bias Out
Algorithms are not neutral. They reflect the data they’re trained on—and the people who design them. That means any systemic bias in the training data, model structure, or decision logic will carry through into outputs.
Examples of Algorithmic Bias Affecting PR:
Sentiment Analysis Fails on Dialects: Standard NLP tools often misread African American Vernacular English (AAVE), sarcasm, or non-Western idioms, labeling neutral or positive posts as negative or “aggressive.”
Visibility Gaps: If an algorithm favors high-engagement content, it may disproportionately amplify majority voices, while sidelining niche or marginalized perspectives, even if those voices are relevant and insightful.
Gendered Language in Media Monitoring: Tools that track coverage sentiment may score stories differently depending on whether they feature male or female spokespeople, due to biased language patterns in training data.
Inaccurate Translation or Localization: AI-powered translation tools can distort meaning in non-English content, misrepresenting the tone or message of global audiences.
When these biases are not recognized, PR teams may make decisions based on flawed insights, overlooking the very people they aim to reach or represent.
Why Inclusivity Matters in Algorithmic PR
1. Brand Authenticity
Brands today are expected to be socially conscious. A campaign that unintentionally excludes or misrepresents a group can lead to public backlash and damage trust.
2. Strategic Accuracy
Flawed data leads to a flawed strategy. If your AI tool underrepresents a demographic or misreads sentiment, your messaging will miss the mark.
3. Equity in Representation
PR professionals help shape public discourse. If the tools they use systematically ignore certain voices, they contribute to a cycle of invisibility for already marginalized communities.
How PR Teams Can Build More Inclusive Algorithms
While few PR pros are coding algorithms themselves, they do have agency over how those tools are selected, used, and audited. Here's how to promote inclusivity at each step of the process:
1. Vet Your Tools for Bias
Ask vendors the tough questions:
What datasets are your algorithms trained on?
How do you ensure representation across languages, dialects, regions, and identities?
What measures are in place to detect and mitigate bias?
If a tool can’t answer these questions, it might not be safe for an inclusive strategy.
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2. Don’t Trust Sentiment Scores Blindly
Sentiment analysis is often flawed, especially when applied to marginalized speech patterns. Instead of taking sentiment scores at face value:
Use them as a starting point, not the whole story.
Pair them with human review, especially for sensitive topics.
3. Include Diverse Voices in Data Analysis
Bring in diverse analysts who can spot cultural context that algorithms miss. A multilingual or multicultural review panel can dramatically improve the accuracy and inclusivity of campaign insights.
4. Center Marginalized Communities Intentionally
If your data input is skewed toward dominant narratives, so will your output be. Actively seek out underrepresented platforms—such as forums, niche blogs, or non-English-language content—when doing social listening.
Some questions to ask:
Whose voices are missing from our data?
What platforms or communities might we be overlooking?
5. Push for Inclusive AI Design
Advocate for more inclusive tech within your organization. Partner with developers and IT teams to build or integrate tools that reflect diverse realities. Encourage collaboration with ethicists, sociologists, and cultural consultants.
6. Conduct Algorithmic Audits
Regularly review how your tools perform across demographics:
Are sentiment results consistent across dialects?
Do gendered names produce different analytics outcomes?
Is regional or linguistic diversity reflected in trend tracking?
These audits can uncover invisible gaps and help correct them.
Case Study: Algorithmic Failure in Action
In 2020, a global fashion brand used an AI sentiment tool to assess feedback on a new inclusive campaign. The algorithm flagged a high rate of “negative sentiment” among Black users on Twitter. On closer inspection, the tool had misclassified neutral uses of AAVE and cultural slang as negative or angry.
Had the PR team acted solely on the data, they might have altered or pulled the campaign. Instead, they enlisted cultural analysts who explained the disconnect, and ultimately used the findings to improve the algorithm and strengthen community relationships.
The lesson? Algorithms need human context. Always.
The Future: Building Ethical AI for PR
The PR industry has an opportunity—and a responsibility—to shape how AI and algorithms are used in brand storytelling. Inclusivity should be built in, not tacked on. That means:
Collaborating with technologists to co-create better tools
Holding vendors accountable for bias
Training PR professionals in data ethics and algorithmic literacy
Imagine a future where PR tools can:
Understand dialectal nuance with accuracy.
Translate meaning, not just words.
Highlight emerging voices from all corners of the web.
Surface patterns across not just demographics, but identities, values, and lived experiences
That’s the inclusive algorithm PR needs—and should demand.
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Final Thoughts: Tech With Intention
Can an algorithm be inclusive? On its own, no. Algorithms are only as inclusive as the humans behind them.
But if PR professionals approach technology with intentionality, cultural humility, and ethical rigor, they can turn tools of exclusion into instruments of inclusion.
It starts with a shift in mindset—from “What can AI do for us?” to “What kind of world do we want our AI to help build?”
Because in the end, inclusion isn’t just a checkbox. It’s a commitment. And for the PR industry, it’s one worth making.
