As we tread deeper into the twenty-first century, artificial intelligence (AI) continues to advance, progressively emulating human cognitive processes. Since its foundational years in 1955, AI has evolved significantly, equipping machines with problem-solving and self-learning abilities once exclusive to humans. The growth of AI is intrinsically linked to the advancements in Machine Learning (ML), a field that flourishes due to voluminous data streams feeding into predictive algorithms. These AI-based models, designed to make independent decisions, herald an era of automation with broad societal and economic implications.

However, the advent of AI is not without its ethical quagmires. From privacy breaches and discriminatory biases to accountability lapses, the ethical conundrums posed by AI raise questions that demand immediate attention. The overarching concern remains that while AI can expedite automation and increase productivity, its societal impacts remain tethered to its applications.

The escalating misuse of AI for nefarious activities, such as voice technology-based frauds and deep fake manipulations, underscores the necessity of ethical AI. Furthermore, AI's incorporation into justice systems can lead to discrimination based on race, gender, and ethnicity, exacerbating social inequities. These ethical concerns necessitate decision-making algorithms that genuinely consider stakeholders' values and post-deployment evaluations to quantify the impact of AI decisions.

Historically, the emergence of AI's ethical quandaries dates back to 1950 with the inception of computer ethics. It wasn't until 1998, however, that the intersection of AI and ethics surfaced as a distinct area of research. Pioneering work by thinkers such as Bostrom, Anderson, and Brundage unraveled the ethical dilemmas inherent in super-intelligent AI and emphasized the importance of embedding ethical consciousness in machines.

Modern studies have further highlighted the ethical concerns surrounding AI, identifying areas of conflict such as privacy, corporate governance, and social consequences. In domains like healthcare and education, researchers underscore the importance of algorithmic transparency, privacy protection, and AI accountability. Ethical risk factors across AI decision-making are now the subject of qualitative research.

AI's ethical conflicts arise from its unpredictability, dependency on large datasets, and inherent ambiguity. Examples of these ethical conundrums include privacy and data confidentiality issues, potential biases leading to discrimination, and the accentuation of societal inequalities. Moreover, AI's use in sensitive sectors like the justice system and its role in autonomous weaponry development magnifies the ethical complexities involved. The realm of healthcare is not immune either, as the incorporation of AI can replace human contact in clinical settings and cause diagnostic errors.

The challenges associated with ethical conflicts in AI are manifold. They encompass technical security, unemployment fears, economic disparities, transparency, bias, automation of ethics, and even moral strength. The stakes are high: we must ensure that AI aligns with human values and moral principles to prevent potentially catastrophic outcomes. Fortunately, governments and organizations worldwide are proposing solutions to tackle these issues.

These proposed solutions stress the importance of ethical, transparent, and accountable AI systems that respect fundamental human rights. Recommendations include promoting algorithmic transparency, implementing legal responsibility, and advocating for global coordination. Suggestions also include cybersecurity risk management, routine checks for discriminatory biases, and legal debates on AI personhood. Other areas of focus include workforce retraining, data privacy, and responsibility attribution.

As AI continues to expand its influence, its integration into society brings along a myriad of challenges and risks. Ensuring human rights are prioritized in this journey is of paramount importance. The regulations and legal policies implemented by various countries serve as a testament to the shared concern regarding AI's ethical quandaries.

Yet, the task is far from over. As we move forward, we need further research to establish ethical guidelines that align with societal values. An emphasis on fairness, explainability, and transparency should underpin our efforts to regulate AI. The adoption of AI should focus on enhancing, not replacing, human intelligence, ensuring that ethical concerns are addressed and that societal implications are considered with every step. As the wave of AI innovation sweeps us forward, let us proceed with caution and consider the ethical constraints with equal urgency.