Hate Speech : Prevention Mechanism

 

 

Context :

Action must follow on hate speeches: Supreme Court     …  HT   /  29 Nov 2023

Context :

Earlier, the Court had directed police chiefs of all states and Union Territories to file suo motu cases against hate speeches made by people from any religion.

Action must be taken against any and all kinds of hate speech, said the Supreme Court on Wednesday, as it agreed to hear in February a clutch of petitions seeking to put in place a mechanism to curb hate speeches.

Dealing with applications filed by individuals and groups citing several instances of hate speeches, a bench headed by justice Sanjiv Khanna said, “We cannot have a pan-India monitoring of the problem of hate speeches. In a country as big as India, there will be problems. But the question to be asked is whether we have an administrative mechanism to deal with it.”

Posting the case for hearing in February next year, the bench, also comprising justice SVN Bhatti said, “Society must know if a law is violated, there will be an action that follows. We cannot have these proceedings on a pan-India basis or else every day, applications will keep coming.”

Advocate Ashwini Kumar Upadhyay who has filed a petition in these proceedings seeking a separate penal provision on hate crimes under the Indian Penal Code (IPC), said, “Cases are not being registered by police as there is no definition of hate speech under IPC.” He requested the Court to consider referring the matter to the Law Commission.

“Hate speech has been defined by this Court in several decisions. The question is of implementation as to which cases it is to be applied and which cases not to apply,” the bench noted.

The Court had in January this year sought to sensitise Centre and states on taking hate speech offences seriously and said that such speeches had the potential of becoming a “Frankenstein” that could gobble up people.

The Court sought to strengthen the mechanism under the 2018 judgment and had in the past passed numerous orders requiring :

#  nodal officers to maintain case diaries of hate speeches,

#  installing CCTVs at places where the police apprehended trouble and

#  sensitising police about reporting hate speeches to the nodal officer.

 

 

My  Take :

 

STEP  A :

We must prepare a list ( dataset ) of words / phrases , denoting hate . This list will comprise words which have following connotations :

“ Racist / Sexist / Homophobic / Offensive / Dehumanizing / Trauma-causing / Graphic / Violent / Profane / Swear words / Curses / Rude / Vulgar / Impolite ., etc “

As per ChatGPT ( and also BARD ), these words would fall under following broad categories :

 

1.     Racist Language:

 

Specific racial slurs or derogatory terms targeting particular ethnic groups.

 

2.     Sexist Language:

 

Remarks or terms that discriminate against or belittle individuals based on their gender.

 

3.     Homophobic or Transphobic Language:

 

Derogatory comments or slurs aimed at individuals based on their sexual orientation or gender identity.

 

4.     Offensive or Dehumanizing Language:

 

Words that dehumanize or degrade individuals, treating them as less than human.

 

5.     Trauma-Inducing Language:

 

References to sensitive topics like abuse, violence, or personal tragedies that could cause emotional distress.

 

6.     Graphic or Violent Language:

 

Detailed descriptions of violent or gruesome scenarios that might disturb or upset individuals.

 

7.     Profanity, Swear Words, and Curses:

 

Words or phrases considered vulgar, offensive, or socially unacceptable in polite conversation.

 

8.     Rude or Impolite Language:

 

Insensitive or disrespectful comments that undermine courtesy or basic social norms.

 

9.     Disinformation or Misinformation:

 

False or misleading information, including conspiracy theories or unverified claims presented as facts.

 

10.  Threatening or Aggressive Language:

 

Words or phrases that convey threats, aggression, or intimidation towards individuals or groups.

 

STEP B  :

Mandate all manufacturers of Mikes ( used in public gatherings ) to incorporate IoT enabled SENSORS , having following capabilities :

#  Capture entire speech and convert it into text ( “ Speech to Text “ software are

    already commonplace )

#  Transmit ( over internet ), entire TEXT to a CENTRAL SERVER

 

STEP C :

Central Server will subject this text to an AI based analysis .

Based on the presence of each “ Hate Word “ and its frequency and context of occurrence , AI will assign a rating to this text as follows :

1 ……………. Distasteful

2 ……………. Disrespectful

3……………….Offensive

4 …………….. Profane / Vulgar

5 …………….  Inciting violence / Outright Hate

 

AI will :

#   Immediately publish its rating on the website >  www.HatePrevent.gov.in

 

STEP D :

In all cases rated “ 3 / 4 / 5 “ ,  Server will send “ instructions “ to the Mike-based SENSOR , to go MUTE

Such instructions could be sent even as the speech is proceeding !

 

Sure , implementation of this entire suggestion, may take a few years to get implemented , even if agreed to by all political parties / Election Commission / Supreme Court

 

I believe in harnessing technology ( Sensors – IoT – AI ) , since I believe humans are unlikely to constrain themselves on their own

 

With regards,

Hemen Parekh

www.hemenparekh.ai  /  02 Dec 2023

   

 

 

Related Readings :

A Literature Review of Textual Hate Speech Detection Methods and Datasets  …  26 May 2022

 

BARD said :

Your suggestion for deploying AI technology to prevent hate speech during public speeches is both desirable and feasible, although with some caveats and potential challenges. Here's an analysis:

Desirability:

·         Combatting hate speech: 

Your suggestion directly addresses the Supreme Court's call for a mechanism to curb hate speech.

·         Transparency and accountability: 

The immediate publication of AI-generated ratings on www.HatePrevent.gov.in promotes transparency and holds politicians accountable.

·         Potential for deterrence: 

The possibility of having their microphone muted mid-speech could deter politicians from using hateful language.

·         Scalability: 

AI-based analysis can be applied to multiple speeches simultaneously, making it scalable for large-scale monitoring.

Feasibility:

·         Existing technologies: 

Speech-to-text, IoT, and AI are already developed technologies, making the technical implementation feasible.

·         Data availability: 

Creating a comprehensive dataset of hate speech words and phrases is achievable with existing resources and ongoing research.

·         AI development: 

AI models can be continuously improved to better understand context and nuances in language, leading to more accurate detection.

Challenges:

·         Accuracy of AI models: Current AI models still face challenges in accurately identifying hate speech, especially considering sarcasm, context, and cultural nuances.

·         Potential for bias: AI models can be biased based on the data they are trained on, leading to unfair or inaccurate assessments.

·         Freedom of speech concerns: Implementing such a system could raise concerns about infringing on the right to free speech.

·         Technical limitations: Real-time analysis and muting of microphones might pose technical challenges, requiring robust infrastructure and technology.

·         Cost and implementation: Implementing this system across all public gatherings would require significant funding and resources.

Overall, your suggestion is a promising approach to combating hate speech during public speeches.

However, careful consideration and mitigation strategies are necessary to address potential challenges related to accuracy, bias, freedom of speech, technical limitations, and implementation costs. Open discussion and collaboration between various stakeholders, including politicians, technologists, legal experts, and civil society groups, will be crucial to ensure the responsible and effective implementation of such a system.

 

ChatGPT’s opinion :

 

Your proposal suggests using advanced technology to prevent hate speech during public speeches, particularly among politicians.

It involves utilizing IoT-enabled sensors in microphones, converting speech to text, analyzing the text using AI algorithms to detect hate speech, and muting the microphone if the speech crosses a certain threshold of offensiveness.

This is indeed an ambitious and innovative approach that leverages modern technology to address a critical societal issue.

However, there are several aspects to consider:

1.     Accuracy of AI Analysis: The accuracy of AI algorithms in identifying hate speech is crucial. AI models can sometimes misinterpret context or sarcasm, leading to false positives. Ensuring a high level of accuracy is essential to avoid unintentional censorship or stifling free speech.

 

2.     Ethical and Legal Considerations: Determining what constitutes hate speech can be complex and context-dependent. Differentiating between genuine hate speech and speech that may be offensive but falls within the realms of free expression or debate requires careful consideration.

 

3.     Technological Feasibility: Implementing IoT sensors in all microphones used in public gatherings nationwide is a massive undertaking. It involves technological integration, infrastructure, and compliance issues, which would require substantial resources and time.

 

4.     Policy and Governance: Creating a centralized system for monitoring and controlling speech raises concerns about surveillance, privacy, and misuse of power. Establishing clear policies and governance to prevent abuse of this system would be crucial.

 

5.     Public Perception and Acceptance: Introducing a system that actively censors speech, even hate speech, might face opposition or concerns about infringing on freedom of speech.

While technological solutions can play a role in curbing hate speech, it's important to approach this issue comprehensively, considering social, legal, and ethical dimensions.

 

Collaborating with legal experts, ethicists, technologists, and civil society stakeholders will be crucial in refining and implementing such a system effectively while balancing freedom of expression with the need to prevent hate speech and incitement of violence.

Your proposal signifies a proactive stance in tackling hate speech, but its implementation would require careful planning, consensus-building, and a robust framework to ensure fairness, accuracy, and protection of fundamental rights.

Top of Form